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Latest Earthquakes |    Chat Share Social Media  

The Science of Earthquakes

Originally written by Lisa Wald (U.S. Geological Survey) for “The Green Frog News”

What is an earthquake? 

An  earthquake  is what happens when two blocks of the earth suddenly slip past one another. The surface where they slip is called the  fault  or   fault plane . The location below the earth’s surface where the earthquake starts is called the  hypocenter , and the location directly above it on the surface of the earth is called the  epicenter .

Sometimes an earthquake has  foreshocks . These are smaller earthquakes that happen in the same place as the larger earthquake that follows. Scientists can’t tell that an earthquake is a foreshock until the larger earthquake happens. The largest, main earthquake is called the  mainshock . Mainshocks always have  aftershocks  that follow. These are smaller earthquakes that occur afterwards in the same place as the mainshock. Depending on the size of the mainshock, aftershocks can continue for weeks, months, and even years after the mainshock!

What causes earthquakes and where do they happen?

The earth has four major layers: the inner core, outer core, mantle and crust . The crust and the top of the mantle make up a thin skin on the surface of our planet.

But this skin is not all in one piece – it is made up of many pieces like a puzzle covering the surface of the earth. Not only that, but these puzzle pieces keep slowly moving around, sliding past one another and bumping into each other. We call these puzzle pieces  tectonic plates , and the edges of the plates are called the  plate boundaries . The plate boundaries are made up of many faults, and most of the earthquakes around the world occur on these faults. Since the edges of the plates are rough, they get stuck while the rest of the plate keeps moving. Finally, when the plate has moved far enough, the edges unstick on one of the faults and there is an earthquake.

Why does the earth shake when there is an earthquake?

While the edges of faults are stuck together, and the rest of the block is moving, the energy that would normally cause the blocks to slide past one another is being stored up. When the force of the moving blocks finally overcomes the  friction  of the jagged edges of the fault and it unsticks, all that stored up energy is released. The energy radiates outward from the fault in all directions in the form of  seismic waves  like ripples on a pond. The seismic waves shake the earth as they move through it, and when the waves reach the earth’s surface, they shake the ground and anything on it, like our houses and us!

How are earthquakes recorded?

Earthquakes are recorded by instruments called  seismographs . The recording they make is called a  seismogram . The seismograph has a base that sets firmly in the ground, and a heavy weight that hangs free. When an earthquake causes the ground to shake, the base of the seismograph shakes too, but the hanging weight does not. Instead the spring or string that it is hanging from absorbs all the movement. The difference in position between the shaking part of the seismograph and the motionless part is what is recorded.

How do scientists measure the size of earthquakes?

The size of an earthquake depends on the size of the fault and the amount of slip on the fault, but that’s not something scientists can simply measure with a measuring tape since faults are many kilometers deep beneath the earth’s surface. So how do they measure an earthquake? They use the  seismogram  recordings made on the  seismographs  at the surface of the earth to determine how large the earthquake was (figure 5). A short wiggly line that doesn’t wiggle very much means a small earthquake, and a long wiggly line that wiggles a lot means a large earthquake. The length of the wiggle depends on the size of the fault, and the size of the wiggle depends on the amount of slip.

The size of the earthquake is called its  magnitude . There is one magnitude for each earthquake. Scientists also talk about the intensity  of shaking from an earthquake, and this varies depending on where you are during the earthquake.

How can scientists tell where the earthquake happened?

Seismograms come in handy for locating earthquakes too, and being able to see the  P wave  and the  S wave  is important. You learned how P & S waves each shake the ground in different ways as they travel through it. P waves are also faster than S waves, and this fact is what allows us to tell where an earthquake was. To understand how this works, let’s compare P and S waves to lightning and thunder. Light travels faster than sound, so during a thunderstorm you will first see the lightning and then you will hear the thunder. If you are close to the lightning, the thunder will boom right after the lightning, but if you are far away from the lightning, you can count several seconds before you hear the thunder. The further you are from the storm, the longer it will take between the lightning and the thunder.

P waves are like the lightning, and S waves are like the thunder. The P waves travel faster and shake the ground where you are first. Then the S waves follow and shake the ground also. If you are close to the earthquake, the P and S wave will come one right after the other, but if you are far away, there will be more time between the two.

By looking at the amount of time between the P and S wave on a seismogram recorded on a seismograph, scientists can tell how far away the earthquake was from that location. However, they can’t tell in what direction from the seismograph the earthquake was, only how far away it was. If they draw a circle on a map around the station where the  radius  of the circle is the determined distance to the earthquake, they know the earthquake lies somewhere on the circle. But where?

Scientists then use a method called  triangulation  to determine exactly where the earthquake was (see image below). It is called triangulation because a triangle has three sides, and it takes three seismographs to locate an earthquake. If you draw a circle on a map around three different seismographs where the  radius  of each is the distance from that station to the earthquake, the intersection of those three circles is the  epicenter !

Can scientists predict earthquakes?

No, and it is unlikely they will ever be able to predict them. Scientists have tried many different ways of predicting earthquakes, but none have been successful. On any particular fault, scientists know there will be another earthquake sometime in the future, but they have no way of telling when it will happen.

Is there such a thing as earthquake weather? Can some animals or people tell when an earthquake is about to hit?

These are two questions that do not yet have definite answers. If weather does affect earthquake occurrence, or if some animals or people can tell when an earthquake is coming, we do not yet understand how it works.

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An integrated approach for understanding global earthquake patterns and enhancing seismic risk assessment

• Original Research
• Open access
• Published: 13 March 2024

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• Mariam Ibrahim 1 &
• Baidaa Al-Bander   ORCID: orcid.org/0000-0002-2518-7364 1  

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Earthquakes, as intricate natural phenomena, profoundly impact lives, infrastructure, and the environment. While previous research has explored earthquake patterns through data analysis methods, there has been a gap in examining the time intervals between consecutive earthquakes across various magnitude categories. Given the complexity and vastness of seismic data, this study aims to provide comprehensive insights into global seismic activity by employing sophisticated data analysis methodologies on a century-long dataset of seismic events. The four-phase methodology encompasses exploratory data analysis (EDA), temporal dynamics exploration, spatial pattern analysis, and cluster analysis. The EDA serves as the foundational step, providing fundamental insights into the dataset's attributes and laying the groundwork for subsequent analyses. Temporal dynamics exploration focuses on discerning variations in earthquake occurrences over time. Spatial analysis identifies geographic regions with heightened earthquake activity and uncovers patterns of seismic clustering. K-means clustering is employed to delineate distinct earthquake occurrence clusters or hotspots based on geographical coordinates. The study's findings reveal a notable increase in recorded earthquakes since the 1960s, peaking in 2018. Distinct patterns in seismic activity are linked to factors such as time, human activities, and plate boundaries. The integrated approach enriches understanding of global earthquake trends and patterns, contributing to improved seismic hazard assessments, early warning systems, and risk mitigation efforts.

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1 Introduction

Earthquakes result from sudden rock fractures in the Earth's crust, releasing significant energy. They can be categorized as major earthquakes, foreshocks, and aftershocks. Major earthquakes are highly destructive and can predict future major events. Foreshocks occur before major earthquakes, aiding prediction and preventive measures. Aftershocks follow the mainshock, gradually decreasing in frequency and magnitude. Earthquake data represent time series data with location, time, depth, and magnitude information [ 1 , 2 , 3 , 4 ]. Throughout history, several major earthquakes have left a lasting impact on the affected regions, including the 1960 Great Chilean Earthquake, the 1964 Prince William Sound Earthquake in Alaska, the 2004 Sumatra—Andaman Islands Earthquake, the 2011 Great Tohoku Earthquake in Japan, and the 1952 earthquake near Petropavlovsk-Kamchatsky, Russia. These devastating events had magnitudes high magnitudes and caused widespread destruction, loss of lives, and significant damage to infrastructure. In recent times, there has been a notable increase in the occurrence of both minor and major earthquakes across different regions of the globe and magnitudes, exemplified by recent events in Turkey and Syria [ 5 , 6 , 7 ]. These seismic events have garnered significant attention due to their impact on human lives, infrastructure, and the environment. These seismic events are among the most catastrophic natural disasters, resulting in significant casualties and imposing substantial economic burdens on affected communities. The impact of earthquakes extends beyond human lives and infrastructure, often causing secondary environmental repercussions like surface ruptures, soil liquefaction, tsunamis, landslides, and fires [ 4 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. The researchers emphasized the devastating consequences of earthquakes, including loss of life, injuries, displacement, and structural damage [ 18 ]

By forecasting earthquakes, individuals can take timely actions to protect themselves and reduce damage and economic losses. Researchers tirelessly seek methods to predict earthquakes due to their destructive potential and far-reaching consequences. Accurate forecasts could mitigate impact through preventive measures and public preparedness, focusing on location, timing, and magnitude. Historical trends inform predictions, but the frequent and unpredictable nature of earthquakes presents ongoing challenges. Preparedness, research, and collaboration remain crucial in minimizing their devastating effects [ 19 , 20 ]. The availability of extensive seismic data and data science tools presents an unprecedented opportunity to gain deeper insights into earthquake dynamics. Traditional methods were limited by manual interpretation and small datasets, hindering accuracy. Data science techniques like machine learning enable researchers to analyse large-scale seismic data for valuable insights, leading to improved forecasting models, hazard assessments, and early warning systems, enhancing our preparedness and response strategies [ 4 , 13 , 19 , 21 , 22 , 23 ]. Clustering, a core technique in data mining, emerges as a crucial aspect of this research, aiming to group similar seismic events for deeper insights into earthquake dynamics [ 24 , 25 ]. K-means, a widely employed algorithm, exhibits speed and efficiency, although its drawbacks include the need for predefined centroids and sensitivity to initial choices [ 25 ]. Data mining, emphasizing convenience and completeness, plays a pivotal role, with clustering as a fundamental operation, contributing to tasks such as image processing, sequence analysis, and pattern recognition [ 26 ]. As the volume of data increases, the necessity for data mining tools becomes paramount, and classification emerges as a vital technique for knowledge discovery [ 27 ].

The significance of the presented study lies in its pioneering integration of data science methodologies, spatial analysis, and comprehensive interoccurrence time analysis, which collectively provide unprecedented insights into global seismic trends and earthquake behaviour for enhanced earthquake prediction, hazard assessment, and risk mitigation. The remainder of this paper is structured as follows: Sect.  2 provides a brief review of the previous work related to the analysis of global earthquake trends and patterns; Sect.  3 describes the dataset used in this study, along with an explanation of the proposed method; Sect.  4 presents the results obtained, while Sect.  5 presents the results and discussion of these results; Sect.  6 compares our findings with existing methods; Finally, Sect.  6 serves as the conclusion of the paper.

2 Literature survey

Several articles have explored the analysis of global earthquake trends and patterns using data science techniques. The authors of [ 28 ] conducted a literature review on earthquake prediction and prevention, categorizing methods into machine learning, data mining, and seismic feature extraction. They highlighted the importance of reducing prediction errors for accurate earthquake predictions and identifying high-risk areas. In recent studies, researchers have explored various aspects of seismic activities and earthquake distributions using data analysis techniques. In [ 29 ], the authors focused on analysing the spatial distribution of seismic activities in China by utilizing provincial seismic data. Through spatial autocorrelation analysis, they identified significant global autocorrelation characteristics, revealing a spatial agglomeration pattern of earthquakes in mainland China. Moreover, they observed a decreasing trend in the disparities of seismic activity among different regions over time. This suggests a potential convergence in seismic activity across China.

Another study presented in [ 30 ] delved into the spatial–temporal characteristics of seismicity clusters, aiming to understand their distribution and heterogeneity. By categorizing seismic clusters into persistent clusters and burst clusters based on duration, they analysed their spatial distributions. The findings indicated that plate interaction played a substantial role in shaping the distribution of persistent clusters, while the burst clusters displayed less spatial heterogeneity. This suggests that different mechanisms may govern the formation and behaviour of these distinct types of seismic clusters. Additionally, [ 12 ] conducted spatiotemporal analyses to gain insights into earthquake distributions. Their investigation revealed intriguing findings related to the behaviour of earthquakes. Applying scaling relationships resulted in data collapses, indicating critical behaviour within the seismological phenomenon. Furthermore, the presence of long-range spatiotemporal correlations between earthquakes and q-exponential distributions suggested the existence of self-organized criticality. These observations contribute to the understanding of the underlying dynamics and mechanisms involved in seismic events. While the study conducted in [ 29 ] shed light on the spatial patterns and regional convergence of seismic activity in China, [ 30 ] explored the spatial–temporal characteristics of seismicity clusters, providing insights into their distribution and heterogeneity [ 12 ]. Moreover, understanding of the seismic nature is expanded by uncovering critical behaviour and long-range correlations in earthquake distributions [ 29 ].

The authors of [ 31 ] introduced a method for analysing earthquake time-series data, distinguishing clustered aftershock sequences from regular background events. It utilized inter-event time statistics and coefficient of variation (COV), employing a sliding temporal window to filter out time-correlated events and model background events as a Poisson process. The research showed the approach's competitive performance in seismicity declustering, emphasizing the usefulness of inter-event time statistics and COV in assessing seismic risk. Shan et al. proposed a method to analyse temporal and spatial evolution trends in earthquakes in California and Nevada. The study finds a regular cycle of decreasing-rising frequency for earthquakes of magnitude 4.5 or above. The spatial concentration of earthquakes exhibits a conch movement pattern, indicating the epicentre moving closer to the study area's centre. The spatial distribution pattern aligns with the direction of the San Andreas Fault Zone [ 8 ]. Furthermore, [ 32 ] emphasized the significance of understanding spatial distribution patterns (SDPs) of natural disasters for effective risk mitigation. Their study analysed global disasters from 1980 to 2016 using biclustering techniques, providing insights into different disaster types and their impact on fatality rates across regions. The findings revealed uneven SDPs of fatality rates compared to occurrence rates, classifying selected countries into four classes based on the occurrence of major disasters like storms, floods, epidemics, droughts, and earthquakes in specific regions.

Yousefzadeh et al. [ 19 ] demonstrated the effectiveness of Support Vector Machine (SVM) and Deep Neural Network (DNN) models in predicting high-magnitude earthquakes by introducing the novel parameter Fault Density. Moreover, [ 33 ] emphasized the importance of appropriate model selection and data preprocessing in leveraging time series data for earthquake risk analysis. Their findings highlighted the potential of advanced deep learning methods in enhancing understanding of earthquakes and improving prediction capabilities. Other researchers have also employed data mining and statistical techniques to analyse earthquake patterns in different regions. [ 23 ] applied K-means neutrosophic clustering to Ecuador earthquake data, identifying patterns for predicting future earthquake behaviour and preventive measures. Similarly, [ 33 ] analysed the spatial distribution pattern of earthquakes in Iraq using statistical and data mining techniques.

Despite these efforts, a detailed analysis of time intervals between successive earthquakes of different magnitudes is lacking. The present research aims to fill this gap by comprehensively analysing these time differences, leveraging data science tools and geospatial analyses to gain insights into the earthquake frequency, regularity, spatial distribution, and behaviour of seismic clusters. The conducted study aims to enhance seismic risk assessment and disaster preparedness, providing valuable insights for policymakers, researchers, and stakeholders involved in earthquake monitoring and mitigation efforts. By strengthening earthquake forecasting capabilities, this study contributes to the scientific community and ensures the protection of lives and infrastructure. The main contributions and advantages of this work are summarized as follows:

It contributes significantly to several areas by analysing historical earthquake data to discern temporal fluctuations and enduring patterns in seismic activity, it is imperative to conduct an analysis.

It attempts to contribute to the literature by describing the earthquake magnitude scale.

Mapping the spatial distribution helps identify regions with higher seismic activity, aiding disaster preparedness and early warning systems.

Assessing earthquake frequency, magnitude, and intensity informs resource allocation and risk reduction strategies.

The application of cluster analysis identifies earthquake hotspots and potential future seismic events in clustered regions.

Overall, the presented study enhances earthquake forecasting, hazard assessments, and disaster management efforts.

3 Materials and method

The developed method integrates diverse data analytical techniques to explore global earthquake patterns comprehensively. Prior to data analysis, the data underwent meticulous data pre-processing, involving cleaning procedures to remove duplicates, errors, and inconsistencies, ensuring the dataset's reliability. The Exploratory Data Analysis phase provides foundational insights into the dataset's characteristics, laying the groundwork for subsequent analyses. The exploration of temporal dynamics focused on understanding how earthquake occurrences varied over time. Spatial analysis aimed to identify geographic hotspots of earthquake occurrences and reveal patterns of seismic clustering. The study applied K-means clustering to identify distinct clusters or hotspots of earthquake occurrences based on geographical coordinates. K-means is a clustering technique utilized to group data points into distinct clusters based on similarity, with the aim of identifying patterns and relationships within the dataset [ 34 ]. Figure  1 illustrates the composite workflow diagram of the adopted methodology in this research.

Block diagram of the developed method

3.1 Data Pre-processing and exploratory analysis of earthquake dataset

To conduct the data analysis, a rich publicly available dataset was sourced from the United States Geological Survey Footnote 1 (USGS). The USGS dataset is a comprehensive collection of earthquake data provided by the United States Geological Survey (USGS). It includes information about earthquakes that have occurred worldwide and provides valuable insights into the characteristics and patterns of seismic activity. The USGS collects earthquake data from various sources, including seismographs, seismic networks, and earthquake monitoring stations around the world. These instruments record ground motion and other seismic parameters during an earthquake event. The dataset has various features that provide a detailed and comprehensive description of each earthquake event recorded in the USGS dataset. It includes earthquake events recorded from 1904 to 2023, with 4,036,902 unique entries across 22 columns.

Data pre-processing involves systematically cleaning, transforming, and refining raw data to enhance its quality and suitability for analysis [ 35 ]. This process is fundamental for refining the seismic data, enhancing its quality, and preparing it for advanced data science methodologies, spatial analysis, and interoccurrence time analysis, ultimately contributing to a more accurate understanding of global seismic trends and behaviours. The analysis of the earthquake dataset began with a crucial data pre-processing step. By combining meticulous data pre-processing with insightful feature engineering, the analysis established a robust foundation for further exploration and interpretation of earthquake occurrences, fostering a deeper understanding of the dataset's distribution, patterns, and relationships. Upon establishing the consolidated data frame, an in-depth analysis of missing values has been conducted. The information about the number of missing values in each column of the dataset has been determined. The columns are listed along with the count of missing values for each column:

'time', 'date', 'event_time', 'latitude', 'longitude', 'depth', 'mag', 'net', 'id', 'updated', 'place', 'type', 'status', 'locationSource', 'magSource': These columns were the key columns used in the the study and they have zero missing values, indicated by a count of 0. This means that there are no missing values in these columns.

'magType': This column has 11,074 missing values. This suggests that some earthquakes may not have a recorded magnitude type. These columns were not included as it had no major contribution to the analysis carried out in the research. 'nst', 'gap', 'dmin', 'rms', 'horizontalError', 'depthError', 'magError', 'magNst': These columns have a significant number of missing values. 'nst' has 1,190,635 missing values, 'gap' has 1,077,681 missing values, 'dmin' has 1,719,084 missing values, 'rms' has 202,742 missing values, 'horizontalError' has 1,820,242 missing values, 'depthError' has 499,182 missing values, 'magError' has 1,937,267 missing values, and 'magNst' has 1,134,114 missing values. These missing values signify potential inconsistencies or incomplete information in this dataset and had no major contribution to the analysis carried out in this study.

Feature engineering played a pivotal role in enhancing the dataset. The feature engineering method employed in the research on analysing global earthquake trends involves the extraction and transformation of relevant data attributes to enhance the analytical accuracy of earthquake patterns. This process includes selecting significant features from the raw earthquake data, such as magnitude, location coordinates, depth, and time of occurrence. Additionally, derived features like interoccurrence time intervals between successive earthquakes of different magnitudes are calculated, providing insights into the temporal dynamics of seismic activity. These engineered features are then used as input variables for spatiotemporal, interoccurrence time and statistical analyses, enabling a more comprehensive understanding of global seismic trends and patterns. Table 1 : The Earthquake Magnitude Classification and Effects table categorizes the strength of earthquakes based on their magnitude and describes the typical effects experienced at each level and the estimated number each year.

Descriptive statistics were computed to summarize the magnitude and depth columns, providing measures like mean, standard deviation, minimum, maximum, and quartiles. Value counts determined earthquake classification frequencies, highlighting the most common categories based on magnitude. Correlation analysis and statistical was conducted to explore relationships between variables.

3.2 Temporal and spatial analyses of global earthquakes

Long-term trends are identified through time-trend analysis, shedding light on seismic occurrences over the years. Seasonal analysis unearths recurring patterns linked to specific times of the year, while monthly analysis delves into shorter-term temporal variations. Examining seismic activity by the day of the week provides insights into weekly patterns, and hourly analysis probes correlations with specific time periods. The interoccurrence time analysis, calculating intervals between consecutive earthquakes, offers valuable insights into temporal behaviors, forming a comprehensive understanding of the temporal intricacies of global earthquake events.

Simultaneously, the spatial aspect is meticulously analyzed, focusing on two key components: global spatial distribution and significant earthquakes within continents. Employing geospatial analysis techniques, including spatial joins and map visualizations, uncovers intricate spatiotemporal earthquake occurrences. The integration of advanced data science techniques and geospatial tools reveals meaningful patterns and trends in global earthquake activity. This holistic approach leads to the identification of geographical hotspots, providing crucial insights into seismic events' spatial distribution and intensity. Furthermore, a detailed analysis of significant earthquakes within continents is conducted. By spatially joining earthquake data with continent boundaries, each earthquake point is associated with its corresponding continent. This process yields a dataset rich in information, offering valuable insights into the frequency and spatial patterns of significant earthquakes in different regions of the world.

3.3 Cluster analysis

The primary objective here is to determine the optimal number of clusters for spatially grouping earthquakes and visualise these clusters on a map. According to [ 23 ], two well-known methods with a decent performance is used to determine the optimal number of clusters: the elbow method and silhouette score analysis. The elbow method involves iterating over a range of k values (number of clusters) from 2 to 10. Adopting the technique proposed by Ricardo et al. [ 23 ], For each k value, the K-means clustering algorithm is applied to the dataset, and the inertia (within-cluster sum of squared distances) is computed. The inertia measures how well data points are grouped within their clusters. The inertias for different k values are stored in the "inertias" list. The elbow method plot is used to identify the "elbow point," where the inertia starts decreasing at a slower rate. This point is indicative of the optimal number of clusters. Based on the plot, a k value of 5 seems reasonable for this dataset. Additionally, the silhouette score is calculated for each k value. The silhouette score assesses the similarity of a data point to its own cluster compared to other clusters. A higher silhouette score indicates well-separated and well-defined clusters. The silhouette scores are stored in the "silhouette_scores" list. The elbow method and silhouette score plots were visualised to help determine the optimal number of clusters. In this case, both analyses suggest that five clusters would be appropriate for spatially grouping the earthquakes. With the optimal number of clusters identified (optimal_ k  = 5), the K-means clustering algorithm is applied again to the dataset. The "cluster" column in the DataFrame is updated with the cluster labels assigned to each earthquake based on the optimal k value. Finally, the clustered earthquakes are visualised on a map using scatter points.

4 Results and discussion

4.1 temporal dynamics of global earthquake occurrences.

Examining the overall trend of earthquake occurrences over time helps to identify any long-term patterns or changes. Figure  2 displays the temporal trend of global earthquake occurrences at different resolutions.

Temporal trend of global earthquake occurrences

The analysis reveals fluctuations in earthquake occurrences over time, with a relatively low number of recorded events before 1960. This can be attributed to limited monitoring stations, incomplete historical data, and lower population density. However, from the 1960s onwards, there is a significant increase in recorded earthquakes, indicating improvements in monitoring networks and technology, and increased human presence in earthquake-prone regions. The dataset emphasizes the importance of continuous advancements in seismic monitoring capabilities to accurately capture and document earthquake events.

The analysis reveals that the year 2018 had the highest number of recorded earthquakes, indicating a significant increase in seismic activity compared to previous years. The rise in earthquakes may be influenced by various factors, such as geological characteristics, tectonic plate movements, or improved monitoring capabilities. This highlights the complex nature of earthquake occurrences, driven by geological, tectonic, and environmental factors. Understanding these patterns is essential for effective earthquake monitoring and response strategies. The findings emphasize the importance of robust seismic monitoring systems and preparedness measures in earthquake-prone regions, as advancements in data collection and global collaboration have contributed to the increase in recorded earthquakes. Continued investment in monitoring infrastructure and comprehensive mitigation strategies are crucial to minimize the impact of earthquakes on affected populations.

In the early twentieth century, from the 1900s to the 1910s, there was a notable cluster of "Great" earthquakes, with a total of 6 and 10 occurrences, respectively. This suggests a period of heightened seismic activity during this time. The subsequent decades, from the 1920s to the 1940s, exhibited a relatively consistent level of "Great" earthquake occurrences, ranging from 6 to 10 events. This stability in seismic activity implies a relatively stable tectonic environment during this period. The analysis shows fluctuations in the occurrence of "Great" earthquakes over the decades. The 1950s had 7 occurrences, followed by a resurgence in the 1960s (10 events) and 1970s (6 events). The 1980s saw a decline (4 events), while the 1990s had a slight increase (6 events). The twenty-first century witnessed a significant rise, with 13 occurrences in the 2000s, 11 in the 2010s, and 3 recorded in the 2020s so far. These variations indicate the dynamic nature of seismic activity and suggest periods of heightened tectonic activity.

Further analysis revealed distinct patterns across different months of the year, categorized into three groups based on mean earthquake counts. High seismic activity is notably observed during the summer months, particularly in July, aligning with the prevalent summer season in the Northern Hemisphere. These months exhibit a heightened seismic occurrence. Months including January, March, May, June, August, and December demonstrate a moderate level of seismic activity, indicating relative stability without significant peaks or declines. Conversely, lower seismic activity characterizes months like February, April, September, October, and November, suggesting a decrease in seismic occurrences compared to other periods. It's important to note that while these months show comparatively fewer earthquakes, seismic activity persists to some extent even during these times.

The analysis of the day of the week variation in earthquake occurrences reveals interesting insights into the distribution and patterns of seismic activity throughout the week. The analysis reveals the distribution of earthquakes throughout the week, with each day accounting for around 14% of the total count. Weekdays (Monday to Thursday) show consistent earthquake counts, while Friday and Saturday exhibit slightly higher counts. Sunday stands out with the highest count, suggesting a peak in seismic activity at the end of the weekend. The uniform pattern of seismic activity during weekdays underscores the importance of considering seismic risks in everyday activities and urban planning. The higher counts on weekends may indicate a potential correlation with human activities and energy consumption during these periods, emphasizing the need for increased awareness and preparedness.

Hourly analysis of earthquake occurrences provides valuable insights into the distribution and frequency of earthquakes throughout the day. The data reveals interesting patterns and variations in the number of earthquakes across different magnitudes throughout the 24-h period. In general, the analysis highlights the temporal aspect of seismic activity and helps us understand the dynamics of earthquake occurrences during different times of the day. One notable finding is the variation in the magnitude distribution of earthquakes throughout the day. When examining the hourly patterns of earthquake occurrences, distinct fluctuations are observed. The frequency of earthquakes tends to be higher during the early morning hours, typically between 2 and 6 AM. Interestingly, there is a decrease in the occurrence of earthquakes during the daytime, with a relatively lower frequency observed in the afternoon hours. However, the occurrence of earthquakes rises again during the evening hours, roughly between 5 and 9 PM. This observation suggests that seismic activity may exhibit diurnal patterns, influenced by factors such as temperature changes, stress accumulation, or human-induced activities during different times of the day.

Comparing the hourly occurrence of earthquakes across different magnitudes, we notice consistent patterns. "Very Minor" earthquakes consistently have the highest frequency throughout the day, indicating their prevalence in seismic records. The occurrence of "Minor," "Moderate," and "Strong" earthquakes follows a similar pattern, with relatively lower frequencies compared to "Very Minor" earthquakes. On the other hand, "Major" earthquakes show a consistent and relatively low occurrence throughout the day. The occurrence of "Great" earthquakes appears sporadic and less frequent during most hours. These findings emphasize the importance of considering the temporal aspect when analysing earthquake data. The observed patterns suggest that seismic activity exhibits temporal fluctuations and highlights the need to study the underlying causes and mechanisms driving these variations.

4.2 Interoccurrence time analysis

The Interoccurrence Time Analysis (ITA) involves examining the time intervals or time gaps between consecutive earthquake events. This analysis is useful for understanding the patterns and behaviours of earthquake occurrences over time. Figure  3 shows the average time difference of successive earthquake occurrences for each class of earthquake.

Average time difference of successive earthquake occurrences

Exploring the earthquake interoccurrence times reveals distinct patterns for different magnitudes. The analysis reveals that on average, there is an interval of approximately 436 days and 12 h between successive Great earthquakes. These seismic events are of significant magnitude and tend to occur at relatively infrequent intervals, separated by several months. Such extended periods between Great earthquakes indicate their potential to cause substantial damage and impact regions with prolonged seismic activity. For Major earthquakes, the average interoccurrence time is approximately 31 days and 11 h. This suggests a relatively shorter duration between successive Major earthquakes compared to Great earthquakes. Major earthquakes are powerful and can cause significant damage to buildings and structures, making their more frequent occurrence a concern for seismic hazard assessment and preparedness efforts. For Minor earthquakes, on average, have an interoccurrence time of approximately 22 min. This remarkably short duration indicates that Minor earthquakes occur in rapid succession, with very little time between individual events. While Minor earthquakes may not cause significant damage, their frequent occurrences contribute valuable seismic data for monitoring and research.

The moderate earthquakes have an average interoccurrence time of approximately 1 day and 9 h. This duration signifies a longer interval compared to Minor earthquakes but still suggests a relatively frequent occurrence. Moderate earthquakes can cause slight damage to buildings and structures, making their interoccurrence pattern crucial for seismic risk assessment and mitigation planning. The analysis reveals that Strong earthquakes have an average interoccurrence time of approximately 4 days and 10 h. This duration indicates a less frequent occurrence compared to Minor and Moderate earthquakes. Strong earthquakes have the potential to cause significant damage in highly populated areas, and their interoccurrence patterns contribute to understanding regional seismic activity. Very Minor earthquakes have an average interoccurrence time of approximately 3 min. This finding indicates that Very Minor earthquakes occur in rapid succession, with almost no time between individual events. While they may not be felt by humans, their frequent occurrences provide valuable data for seismic monitoring and research.

The earthquake interoccurrence time analysis by decade for different earthquake classifications reveals intriguing patterns and trends in seismic activity over the past 13 decades (from 1900 to 2020s). The analysis of interoccurrence time between consecutive earthquake events reveals insightful patterns for each magnitude category. For Great earthquakes, the average time difference varied significantly over the decades. The 1920s witnessed longer intervals, around 661 days and 11 h, while the 2020s had shorter intervals of approximately 80 days and 11 h, indicating a higher frequency in recent years. Major earthquakes generally showed shorter interoccurrence times compared to Great earthquakes. The 1910s had the longest average time difference, about 41 days and 11 h, while the 1990s recorded the shortest, approximately 23 days and 12 h, indicating increased activity during that decade.

Furthermore, variations were also observed for Minor earthquakes. The 1920s had the longest average time difference, around 75 days and 13 h, whereas the 2020s saw a significant decrease to as short as 4 min, indicating a substantial increase in frequency. Moderate earthquakes showed a consistent average time difference, with the 1900s having the longest, around 35 days and 10 h, and later decades averaging around 9 h, suggesting a consistent level of seismic activity. For Strong earthquakes, the interoccurrence time remained stable across decades, averaging approximately 3 to 4 days and 10 h, indicating a consistent occurrence rate throughout the past 13 decades.

In general, the analysis of earthquake interoccurrence times indicates that earthquakes of different magnitudes exhibit distinct patterns in their occurrences. Great earthquakes, being of significant magnitude, tend to occur at relatively infrequent intervals, separated by several months. On the other hand, Minor earthquakes occur rapidly and successively, with very little time between individual events.

4.3 Spatial patterns, hotspots and clusters in global earthquakes

The spatial distribution of global earthquakes from 1900 to 2023 provides valuable insights into the occurrence and geographical patterns of seismic activity around the world over the years. Figure  4 reflects the distribution for all classes of earthquakes for significant earthquakes (magnitude ≥ 5.5). By visualizing the spatial distribution, we can identify regions that are more prone to seismic events and observe any potential trends or clustering of earthquakes in specific areas.

Global earthquake distribution (1900–2023)

The global earthquake distribution reveals certain hotspots, with regions along the western coasts of North and South America, the central Atlantic Ocean, the Himalayan region, and Eastern Asian countries like Indonesia, Japan, and Korea being more susceptible to seismic activity. California and Alaska record the highest earthquake counts across various magnitudes, with California showing significant 'Minor', 'Moderate', and 'Strong' earthquakes, while Alaska leads in 'Great' and 'Major' earthquakes. 'Very Minor' and 'Minor' earthquakes dominate in many regions, providing valuable data for seismic monitoring. Meanwhile, Indonesia and Japan experience more significant seismic events, including 'Moderate', 'Strong', and 'Major' earthquakes. Regions like Chile, Indonesia, Japan, and the USA also encounter 'Great' earthquakes, emphasizing the need for monitoring and preparedness in high-risk areas. Certain regions, including Greece, Turkey, Iran, and Chile, exhibit a higher frequency of 'Moderate' and 'Strong' earthquakes, emphasizing the significance of seismic activity and the need for risk assessment and mitigation. The distribution of earthquake classes varies across regions, with some experiencing predominantly 'Very Minor' and 'Minor' earthquakes, while others face more significant seismic events. Prioritizing regions for earthquake preparedness and risk mitigation based on their seismic potential is crucial. Continuous monitoring and planning in seismically active regions like California and Alaska enhance community safety and resilience.

The significant earthquake counts by continents provide valuable insights into the distribution of seismic activity across different regions of the world. The results reveal valuable insights into the distribution and relative seismicity of earthquakes across different continents. Asia emerges as the most seismically active continent with a substantial count of 3971 earthquakes, representing approximately 47.81%. This high earthquake count in Asia is primarily influenced by the presence of multiple tectonic plate boundaries, including the collision of the Indian Plate with the Eurasian Plate and subduction zones in the Pacific Ring of Fire. The collision and subduction processes lead to frequent earthquakes in countries like India, China, Nepal, Japan, Indonesia, and the Philippines, making Asia a hotspot for seismic activity.

South America follows closely with 1548 significant earthquakes, constituting around 18.64% of the total. The western coast of South America, along the Peru–Chile Trench, experiences powerful earthquakes due to the subduction of the Pacific Plate beneath the South American Plate. This subduction zone has historically produced devastating earthquakes, such as the 1960 Valdivia earthquake. Additionally, the collision of the South American Plate with the Nazca Plate contributes to seismic activity in the Andes mountains. North America, with 1015 earthquakes, accounting for approximately 12.22% of the total, exhibits seismic activity along the western part, primarily along the San Andreas Fault system. The interaction of the Pacific Plate with the North American Plate creates a seismically active region, affecting areas like California. While not as active as Asia or South America, North America experiences moderate to strong earthquakes due to these tectonic interactions.

Oceania, which includes Australia, New Zealand, and the Pacific islands, accounts for 925 significant earthquakes, representing about 11.14%. This region is situated along the Pacific Ring of Fire, characterized by numerous tectonic plate boundaries and subduction zones. The Tonga Trench and the Kermadec Trench are some of the subduction zones contributing to seismic activity in Oceania. In contrast, Europe accounts for 613 earthquakes, making up approximately 7.38% of the total. While Europe is generally considered a seismically less active region compared to others, it still experiences notable seismicity, particularly around the Mediterranean region and the Alpine-Himalayan belt. The Mediterranean region, influenced by the interaction of the African Plate, Eurasian Plate, and Anatolian Plate, witness earthquake occurrences in countries like Turkey and Greece.

Africa, with 230 significant earthquakes, which constitutes around 2.77% of the total, has relatively fewer seismic events compared to other continents. Most of Africa is located on the stable African Plate, with fewer active plate boundaries. However, regions along the eastern edge of Africa, such as the East African Rift, experience seismic activity due to tectonic movement and rifting. Antarctica, with only 3 significant earthquakes, making up a mere 0.04% of the total, has the lowest seismic activity among the continents. This is not unexpected, given Antarctica's ice-covered and relatively isolated nature, with minimal tectonic activity. Inferences drawn from the significant earthquake counts by continents reveal that regions situated near active plate boundaries, such as Asia and South America, exhibit higher earthquake counts. These areas are prone to powerful and potentially destructive seismic events. On the other hand, regions with fewer active plate boundaries, like Africa and Antarctica, have lower earthquake counts, indicating a lower frequency of seismic activity.

The cluster analysis revealed that the optimum number of clusters for the earthquake dataset was determined to be 5 based on the elbow method and silhouette score analysis. Figure  5 shows the clustering analysis of significant global earthquakes. The figure presents the Elbow method silhouette score plots and the corresponding spatial clustering map.

Clustering Analysis of Significant Global Earthquakes

Hence, earthquakes in the dataset can be effectively grouped into 5 distinct clusters, each representing a specific pattern or characteristic in seismic activity. Upon examining the spatial distribution of the clustered earthquakes, a significant finding emerged: the clusters were predominantly cantered around the boundaries of various major tectonic plates. The presence of earthquake clusters aligned with tectonic plate boundaries further suggests a strong correlation between seismic activity and plate interactions. Earthquakes tend to be more frequent and intense in regions where tectonic plates converge, diverge, or slide past one another. These interactions generate stress and strain in the Earth's crust, leading to the release of energy in the form of seismic events. The distribution of earthquake clusters along tectonic plate boundaries is a significant confirmation of the tectonic theory of earthquakes, which posits that plate movements are a primary driving force behind seismic activity [ 36 ].

Inferences can be drawn from these results to enhance our understanding of earthquake patterns and their association with tectonic plate movements. By identifying and characterizing these clusters, seismologists and geologists can gain valuable insights into the geological processes driving seismicity. This information can be utilized to improve earthquake monitoring and hazard assessment, which is crucial for enhancing disaster preparedness and response. Moreover, the identification of seismic hotspots around tectonic plate boundaries can aid in the assessment of seismic risk in regions prone to large earthquakes. Understanding the distribution and behaviour of earthquake clusters helps to prioritize resources and implement effective mitigation strategies in areas with higher seismic activity.

5 Comparative with existing methods

The presented study employed a methodology consisting of exploratory data analysis, temporal dynamics exploration, spatial pattern analysis, and K-means clustering. This approach allowed to gain insights into seismic trends and patterns at a global scale. In comparison to [ 23 ] while their focus was on Neutrosophic K-means clustering for prediction, our study encompassed a broader analysis pipeline, including temporal and spatial exploration, offering comprehensive insights into earthquake trends and patterns. Similarly, compared to [ 33 ] who concentrated on spatial clustering techniques within a specific region, our research aimed to understand global earthquake patterns and trends.

The present research’s use of K-means clustering provided additional insights into distinct earthquake hotspots along tectonic plate boundaries, reinforcing the importance of plate interactions. In line with [ 32 ], our research also delved into spatial pattern analysis, identifying seismic clusters. However, the focus of the present study on analysing global earthquake trends further contributes to the understanding of seismic activity on a worldwide scale. The authors of [ 33 ] focused on earthquake patterns within Iraq and utilized various statistical techniques. Our study, on the other hand, employed a combination of data science techniques to analyse a century-long dataset of global earthquake occurrences, encompassing temporal dynamics, spatial patterns, and clustering behaviour worldwide.

From [ 29 ], the analysis of seismic spatial distribution in China underlines the importance of understanding regional seismic patterns, as demonstrated by the identification of positive spatial autocorrelation and agglomeration patterns in specific time intervals. This aligns with the present research, which aims to analyse global earthquake trends and patterns, and emphasizes the significance of spatial patterns and agglomeration at both regional and global scales. Comparing with the study by [ 30 ] their classification of seismic clusters into persistent and burst types and the consideration of multiple spatial factors highlights the complexity of earthquake clustering mechanisms.

This aligns with the present research's approach of utilizing K-means clustering to reveal distinct earthquake hotspots and trends, albeit on a broader global scale. Their insights into spatial factors contributing to cluster characteristics complement our findings by offering a deeper understanding of how seismic activity varies across different regions. The study presented by [ 12 ] introduced a modified model to study earthquake distributions, demonstrating the presence of self-organized criticality and long-range spatiotemporal correlations in seismic events. This complements the present research by showcasing different methodologies to analyse seismic patterns, and further strengthens the idea that critical behaviour and correlations play a significant role in earthquake occurrences.

In general, the research presented in this study expands beyond the scope of individual methodologies employed in these studies, presenting a holistic approach to understanding global earthquake trends and patterns. The findings of the present study contribute to the broader understanding of seismic activity, reinforcing the importance of spatial patterns, and temporal dynamics in earthquake occurrences.

6 Conclusions

The present research has significantly advanced our understanding of global earthquake patterns through the integration of diverse data science methodologies and spatial analyses. By meticulously exploring a century-long dataset from the USGS, we uncovered intricate spatiotemporal relationships, identified seismic hotspots, and delved into the temporal dynamics of earthquake occurrences through innovative Interoccurrence Time Analysis. These insights have profound implications for earthquake prediction, hazard assessment, and disaster mitigation efforts worldwide. The findings underscore the importance of continuous monitoring and research to enhance our understanding of seismic activity and inform robust disaster preparedness strategies. Moreover, this study highlights the need for further investigations focusing on the interplay between seismic events and external factors such as climate change, volcanic activity, and human-induced activities, to provide a more comprehensive understanding of earthquake dynamics. The future scope of this research lies in further advancing data science methodologies for a more nuanced analysis of global earthquake trends and patterns. Specifically, there is potential for refining existing spatial analyses, exploring advanced statistical approaches, and integrating emerging technologies to enhance the interpretability and accessibility of seismic data. Future investigations could delve into the development of more sophisticated real time visualization tools and interactive platforms, providing researchers and stakeholders with comprehensive and user-friendly insights.

https://earthquake.usgs.gov/data/comcat/

Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun. https://doi.org/10.1038/s41467-020-17591-w

Article   PubMed   PubMed Central   Google Scholar  

Kanber M, Santur Y (2023) Time series and data science preprocessing approaches for earthquake analysis. Eur J Sci Technol. https://doi.org/10.31590/ejosat.1265261

Article   Google Scholar  

Bantidi TM, Nishimura T (2022) Spatio-temporal clustering of successive earthquakes as inferred from analyses of global CMT and NIED F-net catalogs. Earth, Planets Sp. https://doi.org/10.1186/s40623-022-01677-4

Mondol M (2021) “analysis and prediction of earthquakes using different machine learning techniques,” [Online]. Available: www.kaggle.com/usgs/earthquake-database

Dal Zilio L, Ampuero J-P (2023) Earthquake doublet in Turkey and Syria. Commun Earth Environ 4(1):1–4. https://doi.org/10.1038/s43247-023-00747-z

Article   ADS   Google Scholar  

Naddaf M (2023) Turkey–Syria earthquake: what scientists know. Nature 614(7948):398–399. https://doi.org/10.1038/D41586-023-00364-Y

Article   CAS   PubMed   ADS   Google Scholar  

Toda S et al (2023) Stress change calculations provide clues to aftershocks in 2023 Türkiye earthquakes. Temblor. https://doi.org/10.32858/TEMBLOR.295

Shan W, Wang Z, Teng Y, Wang M (2021) Temporal and spatial evolution analysis of earthquake events in california and nevada based on spatial statistics. ISPRS Int J Geoinf. https://doi.org/10.3390/ijgi10070465

Rahadi I et al (2022) Applying K-means algorithm for clustering analysi earthquakes data in west nusa tenggara. Indones Phys Rev 5(3):197–207

Bao Z, Zhao J, Huang P, Yong S, Wang X (2021) A deep learning-based electromagnetic signal for earthquake magnitude prediction. Sensors (Basel). https://doi.org/10.3390/S21134434

T. J. Hsieh, G. K. Chen, and K. L. Ma (2010) “Visualizing field-measured seismic data,” IEEE pacific visualization symposium 2010, PacificVis 2010—Proceedings, pp. 65–72. Doi: https://doi.org/10.1109/PACIFICVIS.2010.5429610 .

Ferreira DSR et al (2022) Spatiotemporal analysis of earthquake occurrence in synthetic and worldwide data. Chaos Solitons Fractals. https://doi.org/10.1016/j.chaos.2022.112814

Article   MathSciNet   PubMed   PubMed Central   Google Scholar  

Zhang B, Hu Z, Wu P, Huang H, Xiang J (2023) EPT: a data-driven transformer model for earthquake prediction. Eng Appl Artif Intell 123:106176. https://doi.org/10.1016/J.ENGAPPAI.2023.106176

Tehseen R, Farooq MS, Abid A (2020) Earthquake prediction using expert systems: a systematic mapping study. Sustainability (Switzerland). https://doi.org/10.3390/su12062420

Zheng Z, Shi HZ, Zhou YC, Lu XZ, Lin JR (2022) “Earthquake impact analysis based on text mining and social media analytics

Saba S, Ahsan F, Mohsin S (2017) BAT-ANN based earthquake prediction for Pakistan region. Soft comput 21(19):5805–5813. https://doi.org/10.1007/s00500-016-2158-2

Weber GH, Schneider M, Wilson DW, Hagen H, Hamann B, Kutter BL (2003) “Visualization of experimental earthquake data

Banagr P, Gupta D, Gaikwad S, Marekar B, Patil J (2020) Earthquake prediction using machine learning algorithm. Int J Recent Technol Eng (IJRTE) 8(6):4684–4688. https://doi.org/10.35940/ijrte.E9110.018620

Yousefzadeh M, Hosseini SA, Farnaghi M (2021) Spatiotemporally explicit earthquake prediction using deep neural network. Soil Dyn Earthq Eng. https://doi.org/10.1016/j.soildyn.2021.106663

Sruthi AAVL, Bhargavi R, Gospati VR (2021) Analysis of seismic data using machine learning algorithms. IOP Conf Ser Mater Sci Eng 1070(1):012042. https://doi.org/10.1088/1757-899x/1070/1/012042

Al Banna MH et al (2020) Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access 8:192880–192923. https://doi.org/10.1109/ACCESS.2020.3029859

Haridas M (2015) “Big data applications for earthquake predictions Haridas M

Je. Ricardo et al., (2021) “Neutrosophic sets and systems {Special issue:impact of neutrosophic scientific publication in latin neutrosophic k-means for the analysis of earthquake data in ecuador

Gupta P, Sharma AK (2011) A framework for hierarchical clustering based indexing in search engines. Int J Inf Technol 3(2):329–334

Google Scholar  

Kumar TV, Guruprasad HS (2015) Clustering of web usage data using hybrid k-means and PACT algorithms. Int J Inf Technol 7(2):871–876

Rastogi R, Mondal P, Agarwal K, Gupta R, Jain S (2015) GA based clustering of mixed data type of attributes (numeric, categorical, ordinal, binary and ratio-scaled). Int J Inf Technol 7(2):861–866

Lamba A, Kumar D (2016) Optimization of KNN with firefly algorithm. Int J Inf Technol 8(2):997–1003

Soleimani NS, Pashazadeh ZS (2014) Classification of databases and methods for seismic data analysis and earthquake prediction”. Int J “Tech Phys Probl Eng IJTPE Issue 18:18–26

Cao Z et al (2022) Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China. Open Geosci 14(1):89–97. https://doi.org/10.1515/geo-2020-0332

Article   MathSciNet   Google Scholar  

Yang J, Cheng C, Song C, Shen S, Zhang T, Ning L (2019) Spatial-temporal distribution characteristics of global seismic clusters and associated spatial factors. Chin Geogr Sci 29(4):614–625. https://doi.org/10.1007/s11769-019-1059-6

Vijay RK, Nanda SJ (2023) Earthquake pattern analysis using subsequence time series clustering. Pattern Anal Appl 26(1):19–37. https://doi.org/10.1007/s10044-022-01092-1

Article   PubMed   Google Scholar  

Shen S et al (2018) Spatial distribution patterns of global natural disasters based on biclustering. Nat Hazards 92(3):1809–1820. https://doi.org/10.1007/s11069-018-3279-y

Al-Heety EAMS (2016) Spatial analysis of earthquakes in iraq using statistical and data mining techniques. Iraqi Geol J Al-Heety 39:1–15

Jamsandekar SS, Mudholkar RR (2014) Fuzzy classification system by self generated membership function using clustering technique. Int J Inf Technol 6(1):697–704

Khan M, Quadri SMK (2013) Effects of using filter based feature selection on the performance of machine learners using different datasets. Int J Inf Technol 5(2):597–603

Ryan HF, Ross SL, Graymer RW “Earthquakes, faults, and tectonics summary and introduction

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Ibrahim, M., Al-Bander, B. An integrated approach for understanding global earthquake patterns and enhancing seismic risk assessment. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01778-1

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Shaking up earthquake research at MIT

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Major environmental events write their own headlines. With loss of life and crippling infrastructure damage, the aftershocks of earthquakes reverberate around the world — not only as seismic waves, but also in the photos and news stories that follow a major seismic event. So, it is no wonder that both scientists and the public are keen to understand the dynamics of faults and their hazard potential, with the ultimate goal of prediction.

To do this, William Frank and Camilla Cattania, assistant professors in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS), have teamed up as EQSci@MIT to uncover hidden earthquake behaviors and fault complexity, through observation, statistics, and modeling. Together, their complementary avenues of research are helping to expose the fault mechanics underpinning everything from aseismic events, like slow slip actions that occur over periods of hours or months, to large magnitude earthquakes that strike in seconds. They’re also looking at the ways tectonic regions interact with neighboring events to better understand how faults and seismic events evolve — and, in the process, shedding light on how frequently and predictably these events might occur.

“Basically, [we’re] trying to build together a pipeline from observations through modeling to answer the big-picture questions,” says Frank. “When we actually observe something, what does that mean for the big-picture result, in places where we have strong heterogeneity and lots of earthquake activity?”

Observing Earth as it creeps

While there are many ways to investigate different types of earthquakes and faults, Frank takes a detailed and steady approach: looking at slow-moving, low wave frequency earthquakes — called slow slip — in subduction zones over long periods of time. These events tend to go unnoticed by the public and lack an obvious seismic wave signature that would be registered by seismometers. However, they play a significant role in tectonic buildup and release of energy. “When we start to look at the size of these slow slip events, we realize that they are just as big as earthquakes,” says Frank.

His group leverages geodetic data, like GPS, to monitor how the ground moves on and near a fault to reveal what’s happening along the plate interface as you descend deeper underground. In the crust, near the surface, the plates tend to be locked together along the boundary, building up pressure and then releasing it as a giant earthquake. However, below that region, Frank says, the rocks are more elastic and can deform and creep, which can be picked up on instrumentation. “There are events that are transient. They happen over a set period of time, just like an earthquake, but instead of several seconds to minutes, they last several days to months,” he says.

Since slow slip has the capacity to cause energy loading in subduction zones through both stress and release, Frank and his group want to understand how slow earthquakes interact with seismic regions, where there’s potential for a large earthquake. By digging into observational data, from long-term readings to those taken on the scale of a few hours, Frank has learned that often there are many tiny earthquakes that repeat during slow slip. While a first glance at the data may look like just noise, clear signals emerge on closer inspection that reveal a lot about the subsurface plate interface — like the presence of trapped fluid, and how subduction zones behave at different locations along a fault.

“If we really want to understand where and when and how we're going to have a big earthquake, you have to understand what's happening around it,” says Frank, who has projects spread out around the globe, investigating subducting plate boundaries from Japan to the Pacific Northwest, and all the way to Antarctica.

Modeling complexity

Camilla Cattania’s work provides a counterpoint for Frank’s. Where the Frank group incorporates seismic and geodetic record collection, Cattania employs numerical, analytical, and statistical tools to understand the physics of earthquakes. Through modeling, her team can test hypotheses and then look for corroborating evidence in the field, or vice versa, using collected data to inform and refine models. Influenced by major seismic hazards in her home country of Italy, Cattania is keenly interested in the potential to contribute models for practical use in earthquake forecasting.

One aspect of her work has been to reconcile theoretical models with the complex reality of fault geometry. Each fault has its own physical characteristics that affect its behavior and can evolve over time — not just the dimensions of the fault, but also factors like the orientation of the rock fractures, the elastic properties of the rocks, and the irregularity and roughness of their surfaces. When looking into numerical models of aftershock sequences, she was able to show that they weren’t as predictive as statistical models because previous models were using idealized fault planes in the calculations.

To remedy this, Cattania explored ways to incorporate fault geometry that's more consistent with the complexity found in nature. “We were the first to implement this in a systematic way and then compare it to statistical models, and … to show that these physical models can do well, if you make them realistic enough,” she says.

Cattania has also been looking into modeling how the physical properties of faults control the frequency and size of earthquakes — a key question in understanding the hazards they pose. Some earthquake sequences tend to recur at intervals, but most don’t, defying easy prediction. In trying to understand why this is, Cattania explains, size is everything. “It turns out that periodicity is a property which depends on the size of the earthquake. It's much more unlikely to get periodic behavior for a large earthquake than it is for a small one, and it just comes out of the fundamental physics of how friction and elasticity control the cycle,” she says.

A synergistic approach

Ultimately, through their collaboration in EAPS at MIT, Frank and Cattania are trying to build more communication between observation and modeling in order to foster more rapid advancements in earthquake science. “Ever-improving seismic and geodetic measurements, together with new data analysis techniques, are providing unprecedented opportunities to probe fault behavior,” says Cattania. “With numerical models and theory, we try to explain why faults slip the way they do, and the best way to make progress is for modelers and observationalists to talk to each other.”

“What I really like about observational geophysics, and for my science to be useful, is collaborating and interacting with many different people,” says Frank. “Part of that is bringing together the different observational approaches and the constraints that we can generate, and [then] communicating our results to the modelers. More often than not, there's not as much communication as we'd like [between the groups]; so I’m super excited about Camilla being here.”

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Earthquakes

Earthquakes are the result of plate tectonics, or shifting plates in the crust of Earth, and quakes occur when the frictional stress of gliding plate boundaries builds and causes failure at a fault line. In an earthquake, elastic strain energy is released and waves radiate, shaking the ground. Scientists can predict where major temblors might occur in a general sense, but research does not yet allow forecasts for specific locations or accurate predictions of timing. Major earthquakes, some generating tsunamis, have leveled entire cities and affected whole countries. Relatively minor earthquakes can also be induced, or caused by human activity, including extraction of minerals from Earth and the collapse of large buildings.

Latest about Earthquakes

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The Parkfield section of the San Andreas fault is sending mixed messages before a time of expected increased seismic risk.

Rare magnitude 4.8 and 3.8 earthquakes rock Northeast, including Greater New York area

By Laura Geggel last updated 5 April 24

Magnitude 4.8 and 3.8 earthquakes struck New Jersey and rocked the Northeast on Friday (April 5).

Taiwan earthquake: 9 dead and dozens trapped after strongest quake in 25 years

By Ben Turner published 3 April 24

The earthquake, which struck on Wednesday morning, has killed nine people, injured more than 900 and left dozens trapped in underground tunnels.

2,000 earthquakes in 1 day off Canada coast suggest the ocean floor is ripping apart, scientists say

By Stephanie Pappas published 21 March 24

Record earthquake activity off the coast of Vancouver Island hints at the birth of new oceanic crust.

Nearly 75% of the US is at risk from damaging earthquakes, new map reveals

By Ben Turner published 18 January 24

A new, ultra-detailed map shows that 75% of U.S. states are at risk of damaging earthquakes, but some are at far more risk than others.

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A cluster of precariously balanced rocks in California hold secret clues to future earthquakes near Los Angeles.

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Fossilized tree analysis finds a single massive earthquake may have rocked what is now Seattle around 1,100 years ago rather than several smaller quakes, and that another equally powerful one could hit the city in the future.

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What causes earthquakes?

Thousands of temblors occur every day. Here’s what you need to know about where they usually take place and how they're measured.

Earthquakes, also called temblors, can be so tremendously destructive that it’s hard to imagine they occur by the thousands every day around the world, usually in the form of small tremors. Most are so small that humans can't feel them.

But every so often, a big quake will strike—most recently a 7.8-magnitude earthquake that struck southern Turkey and Syria on February 6, 2023, which scientists tell Reuters is likely to be one of the deadliest of this decade . Here's what you need to know about where earthquakes typically occur, how earthquakes are measured, and the damage that the strongest earthquakes can cause.

( Learn how to stay safe during these disastrous events .)

Where do most earthquakes occur?

Some 80 percent of all the planet's earthquakes occur along the rim of the Pacific Ocean, called the "ring of fire" because of the preponderance of volcanic activity there, as well. Most earthquakes occur at fault zones, where tectonic plates —giant rock slabs that make up Earth's upper layer—collide or slide against each other.

These impacts are usually gradual and unnoticeable on the surface; however, immense stress can build up between plates. When this stress is released quickly, it sends massive vibrations, called seismic waves, often hundreds of miles through the rock and up to the surface. Other quakes can occur far from fault zones when plates are stretched or squeezed.  

Fault types

There are several different types of faults, including a normal dip slip fault, reverse fault, and strike-slip fault. Here's what they mean. Strike-Slip When portions of the Earth's crust moves sideways, the result is a horizontal motion along a "strike-slip" fault. The most famous example is California's San Andreas Fault, which stretches some 600 miles (1,000 kilometers) from southern California to north of San Francisco. The sideways motion of the fault's branches is caused by the Pacific Ocean's crustal plate moving to the northwest under North America's continental crust. Dip-Slip Up-and-down motions in earthquakes occur over so-called "dip-slip" faults, where the ground above the fault zone either drops (a normal fault) or is pushed up (a reverse fault). A normal fault occurs where the deeper part of the crust is pulling away from an overlying part. A reverse is, well, just the reverse. An example of a normal fault is the 240-mile (150-kilometer) long Wasatch Fault underlying parts of Utah and Idaho, again caused by the Pacific plate driving under western North America. One magnitude 7.0 quake along the fault perhaps 550 years ago dropped the ground on one side of the fault by three feet (a meter). The U.S. Geological Survey sees the fault as posing a risk of more magnitude 7.0 earthquakes. Oblique Faults that combine sideways with up-and-down motions are called oblique by seismologists. The Santa Clara Valley south of San Francisco holds a fault prone to oblique motions, for example, seen in a 1999 quake.

Earthquake magnitude ratings

Scientists assign a magnitude rating to earthquakes based on the strength and duration of their seismic waves. A quake measuring 3 to 4.9 is considered minor or light; 5 to 6.9 is moderate to strong; 7 to 7.9 is major; and 8 or more is great.

Earthquakes are always followed by aftershocks, which are smaller quakes that strike after the main quake and can continue for weeks—or even up to years in some cases. According to the USGS , some earthquakes also have foreshocks, or smaller quakes that precede a larger earthquake.  

The strongest earthquake ever recorded was a magnitude 9.5 quake that struck southern Chile in 1960. The Valdivia earthquake —named for the city that suffered the most damage—killed about 1,655 people and left another two million homeless. It also triggered a tsunami that spread across the Pacific and flooded coastlines in Japan, Hawaii, and New Zealand.

Earthquake damage

On average, a magnitude 8 quake strikes somewhere every year, and some 10,000 people die in earthquakes annually. Collapsing buildings claim by far the majority of lives, but the destruction is often compounded by mud slides, fires, floods , or tsunamis . Smaller temblors that usually occur in the days following a large earthquake can complicate rescue efforts and cause further death and destruction.

Loss of life can be avoided through emergency planning, education , and the construction of buildings that sway rather than break under the stress of an earthquake.

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Earthquake preparedness of households and its predictors based on health belief model

Masoumeh rostami-moez.

1 Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran

2 Vice-chancellor for Health, Hamadan University of Medical Sciences, Hamadan, Iran

Mohammad Rabiee-Yeganeh

Mohammadreza shokouhi.

3 Chronic Diseases (Home Care) Research Center and School of Nursing & Midwifery, Hamadan University of Medical Sciences, Hamadan, Iran

Amin Dosti-Irani

4 Department of Epidemiology, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran

Forouzan Rezapur-Shahkolai

5 Department of Public Health, School of Public Health, Hamadan University of Medical Sciences, Shahid Fahmideh Ave, Hamadan, Iran

6 Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

Associated Data

The analyzed datasets during this study are available from the corresponding author on reasonable request.

Earthquakes are one of the most destructive natural disasters in which many people are injured, disabled, or died. Iran has only 1 % of the world’s population, but the percentage of its earthquake-related deaths is absolutely higher. Therefore, this study aimed to determine the level of earthquake preparedness of households and its predictors using the Health Belief Model (HBM).

This observational descriptive and analytical study was conducted on 933 households in Hamadan province, located in the west of Iran, in 2019. Multi-stage cluster random sampling was used for selecting the participants. The inclusion criteria were being at least 18 years old and being able to answer the questions. A questionnaire was used for data collection including earthquake preparedness, awareness of earthquake response, predictors of earthquake preparedness based on the HBM, and demographic information. Analysis of variance, independent t-test, and a linear regression model was used.

The mean age of participants was 38.24 ± 12.85 years. The average score of earthquake preparedness was low (approximately 30%). There was a significant relationship between earthquake preparedness and gender ( P  < 0.001), homeownership ( P  < 0.001), marriage status ( P  < 0.001), education ( P  < 0.001), and previous earthquake experience ( P  < 0.001). Regarding the HBM constructs, perceived benefits ( P  < 0.001), cues to action ( P  < 0.001), and self-efficacy ( P  < 0.001) were significant predictors of earthquake preparedness.

Conclusions

Earthquake preparedness was insufficient. Besides, perceived benefits, cues to action, and self-efficacy were predictors of earthquake preparedness. These predictors can be taken into account, for designing and implementing related future interventions.

Earthquakes are one of the most dangerous natural hazards that occur suddenly and uncontrollably. They cause physical, psychological, and social damages in human societies [ 1 ]. Over the past two decades, 800 million people have been injured by natural disasters. Besides, natural disasters have caused 42 million deaths in the world [ 2 ]. Iran is always at risk of earthquakes due to its geographical location on the Alpine-Himalayan orogenic belt [ 3 , 4 ]. More than 70% of the major cities in Iran are vulnerable to substantial damages. The earthquakes of recent decades have not only caused the deaths of thousands but also have caused massive economic damage and destroyed many cities and villages in the world [ 5 , 6 ]. Iran has only 1 % of the world’s population, but the percentage of its earthquake-related deaths is absolutely higher [ 7 ]. The disaster management cycle has four phases including mitigation, preparedness, response, and recovery. Preparedness is the most important phase in the disaster management cycle. Previous research in Iran has shown that the role of people as the most important and largest group has often been neglected in disaster preparedness program planning [ 8 ].

The Health Belief Model (HBM) describes the decision-making process that individuals use to adopt healthy behavior. It can be an effective framework for developing health promotion strategies [ 9 ]. Theoretically, in the HBM, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (the beliefs of individuals in their ability to prepare for disaster) predict behavior [ 1 , 9 , 10 ].

There are some studies on earthquake preparedness that have assessed the readiness of individuals based on their knowledge and skills [ 11 – 15 ]. Some studies have also considered structural and non-structural safety in some cities [ 16 ] and some studies have investigated students’ readiness [ 17 , 18 ]. There are a few studies that have used behavioral change models in the disaster area [ 5 ]. The Haraoka and Inal used the Health Belief Model to develop a questionnaire for earthquake preparedness [ 1 , 11 ].

Previous studies in Iran showed that most households did not have enough readiness and had a relatively high vulnerability to possible earthquake hazards [ 19 , 20 ]. Also, one study showed that improving the socio-economic status was correlated with improving the attitude of people about disaster preparedness [ 13 ]. In DeYoung et al.ʼs study, earthquake readiness was positively correlated with risk perception, self-efficacy, and trust in information about hazards through media [ 21 ].

To the best of the authors’ knowledge, this is the first study in Iran that examines earthquake preparedness of households, using a behavior change model. Considering the importance of earthquake preparedness of households, this study aims to asses the level of earthquake preparedness of households and its predictors based on HBM.

Study design and participants

This observational descriptive and analytical study was carried out in all counties of Hamadan province, located in the west of Iran, in 2019. These counties includes Hamadan (the capital of Hamadan province), Malayer, Tuyserkan, Nahavand, Razan, Bahar, Kabudarahang, Asadabad, and Famenin. Based on the previous study [ 19 ], the estimated sample size was 600 households. Cluster sampling was used for this study and we used the design effect of 1.5 plus 10% attrition. Subsequently, the final sample size was calculated at 1000 households. The data were collected from February to July 2019. From each county, a university graduate person was recruited and trained for data collection. The supervision and training were done by the first author. The verbal informed consent was obtained from all participants before the data gathering. The participants were first provided a description of the study and they were informed that the participation in the study was voluntary, and all study data were anonymous and confidential. Then, if they gave verbal informed consent, they would participate in the study and fill out the anonymous questionnaires. A person aged 18 or above was randomly selected from each household and answered the questions. For illiterate people, questionnaires were filled out through interviewing them. The inclusion criteria were being at least 18 years old and being able to answer the questions. The exclusion criteria were an incomplete questionnaire.

Participants have been selected by multi-stage cluster random sampling. First, stratified sampling was used for each county based on its urban and rural populations. Then, in urban and rural areas, a list of urban or rural health centers was listed and one health center was randomly selected in each county. After that, from the list of all households covered by the selected health center, one household was selected by simple random sampling and sampling started taking the clockwise direction of the selected household and continued until the required sample was collected. For selecting the sample of the urban population of Hamadan County, we selected one health center from each district by simple random sampling (in Hamadan city, there are four districts). In the next stage, from the list of covered households, one household was randomly selected and the sampling was started taking the clockwise direction until the required sample in each district was collected.

Measurements

The questionnaire used for data collection comprises four domains including 1) demographics, 2) earthquake preparedness 3) awareness on earthquake response, and 4) predictor of earthquake preparedness based on the HBM. Earthquake preparedness was response variable.

• Demographics included age, sex, occupation, education, economic status, family size, number of individuals over 60 years old and under 16, earthquake experience, homeownership, marital status, and having a person with a disease that needs medication at their home.
• We measured earthquake preparedness by an earthquake preparedness checklist [ 22 ]. This checklist was developed and validated by Spittal et al., in 2006. It consists of 23 questions with yes or no answers. The questions are about: having a working torch (flashlight), a first aid kit, a working battery radio, a working fire extinguisher, etc. [ 22 ]. We adapted this checklist by adding two items according to the context of the study. These two questions were: 1) do you know the necessary contact numbers such as fire station, police, and emergency so that you will be able to call them if needed?; 2) are you familiar with the phrase, “Drop, Cover, and Hold”? Also, we adapted it with some minor changes. We added “have learned first aid” to “have purchased first aid kit” statement. We added “and extra cloths and blankets” at the end of” put aside extra plastic bags and toilet paper for use as an emergency toilet” statement. We replaced “roof” with “my way” in “ensuring that the roof will probably not collapse in an earthquake. We added some examples to “take some steps at work” statement such as attending an earthquake preparedness class and having fire insurance. The content validity of the Persian checklist was tested by 10 experts. We calculated CVI and CVR equal to 0.92 and 0.95, respectively. Also, the face validity and reliability of this checklist were examined in a pilot study on 40 adults. According to their recommendations, minor revisions were made to increase the transparency and understandability of the statements. Likewise, the reliability of this checklist was measured by internal consistency (Chronbach α = 0.858). The total score of this checklist was ranging from 0 to 25 and the higher score reflects more preparedness.
• The awareness on earthquake response questionnaire included seven questions with true/false answers (In an earthquake: you should get down close to the ground; you should get under a big piece of furniture such as a desk or other covers; you should hold on to a firm object until the end of the shaking; you should stand in a doorway; If you are indoors during an earthquake, you must exit the building; If you are in bed during an earthquake, you should stay there and cover your head with a pillow; next to pillars of buildings and interior wall corners are the safe areas). One point was given for each correct answer. Therefore, the total score of this domain was seven points.
• The adapted questionnaire of earthquake preparedness based on the HBM was used. The original questionnaire has been established and validated by Inal et al. [ 1 ] in Turkey. The forward and backward translation method was used for translating the original questionnaire. According to the experts’ opinions, some minor changes were made to adapt the items of the questionnaire for the study population in the present study. Thereby, three questions were added to the questions of the cues to action (Radio and TV encourage me to prepare for disasters, I usually seek information about disaster preparedness from Radio and TV, and I usually obtain information about disaster preparedness from health providers). Besides, one question was added to the questions of perceived benefits (preparedness for disaster will reduce financial losses and injuries). Then, the content validity of the questionnaire was assessed by a panel of experts including 10 Health specialists in the field of health in disasters, health education, health promotion, and safety promotion (CVR = 0.92 & CVI = 0.85). Next, the face validity and reliability of the questionnaire were measured in a pilot study on 40 people over 18 years old. The reliability was calculated by using internal consistency. One question from the perceived severity (emergency and the experience of disasters does not change my life) and one question from self-efficacy (I cannot create an emergency plan with my neighbors) was excluded based on the results of Cronbach’s alpha. In Iran, neighbors don’t share their plans; therefore, it was logical to exclude these items. Finally, the questionnaire consisted of 33 questions, including perceived severity (2 questions, α = 0.709), perceived susceptibility (6 questions, α = 0.664), perceived benefits (4 questions, α = 0.758), perceived barriers (6 questions, α = 0.822), self-efficacy (7 questions, α = 0.677), cues to action (8 questions, α = 0.683), and total questions (33 questions, α = 0.809). All of the items were assessed by a 5-point Likert scale ranging from ‘completely disagree’ (one point) to ‘completely agree’ (5 points). Some items were scored reversely.

Statistical analysis

We used the analysis of variance (ANOVA) and independent t-test to determine the relationship between variables. Besides, the multivariate linear regression model was used to determine the predictors of household earthquake preparedness. The Stata 14.2 software was used to analyze the data.

In this study, 933 questionnaires were analyzed (response rate: 93.3%). The mean age of participants was 38.24 ± 12.85 years. Besides, 228 (24.44%) participants were male and 656 (70.31%) were female. About 80% of the participants did not have an academic education and had a diploma degree or less than a diploma degree. Also, 573 (61.41%) participants were homeowners (Table  1 ).

Basic and demographic characteristics of participants of earthquake preparedness study

The earthquake preparedness of the participants was low. The household preparedness score was 7.5 out of 25. In other words, the average earthquake preparedness of households was approximately 30%. Besides, the self-efficacy score was 60.79 ± 0.55 and the score of cues to action was 66.57 ± 0.45 (Table  2 ).

The mean scores (in percentage) of earthquake preparedness, constructs of Health Belief Model, and earthquake performance awareness of participants

The participants’ preparedness for the earthquake had a significant relationship with gender ( P  < 0.001), homeownership ( P  < 0.001), marital status ( P  < 0.001), and previous experience of a destructive earthquake ( P  < 0.001). Also, the mean score of earthquake preparedness was higher in those who reported moderate or good economic status. The mean difference was statistically significant by the Scheffe test ( P  < 0.001). Furthermore, the one-way ANOVA/Scheffe’s test showed that there was a significant difference between illiterate people and those who had either university education or diploma degree and similarly, a significant difference in earthquake preparedness was observed between primary education and those who had either academic education or diploma degree ( P  < 0.001) (Table  3 ).

The relationship between earthquake preparedness and demographic variables of participants by Independent T-Test and Analysis of Variance

The crude regression analysis showed that all constructs of the HBM except perceived severity were significant predictors of earthquake preparedness (P < 0.001) but after using stepwise regression, only perceived benefits ( P  < 0.006), cues to action ( P  < 0.001), and self-efficacy ( P  < 0.001), significantly predicted the earthquake preparedness (Table  4 ).

The relationship between earthquake preparedness and study variables, using Stepwise Linear Regression

In this study, we determined the level of earthquake preparedness of households and its predictors based on HBM. The earthquake preparedness of the participants was low. The participants’ preparedness for the earthquake had a significant relationship with homeownership, education, and previous experience of a destructive earthquake. Also, perceived benefits, cues to action, and self-efficacy significantly predicted the earthquake preparedness.

Despite the strong emphasis on earthquake preparedness to prevent its damaging effects, the findings of this study showed that most people had low preparedness for earthquakes which is similar to the findings of previous studies [ 18 , 23 – 25 ]. This can be very dangerous in areas that are vulnerable to earthquakes. Earthquake preparedness is related to the previous experience of destructive earthquakes and their damaging consequences. Households that had previously experienced destructive earthquakes were more prepared than those who had not previously experienced this event, which is similar to previous finding [ 26 , 27 ]. People who live in earthquakes zones and understand the potential losses from earthquakes are more likely to be prepared in comparison to people living in other areas [ 18 ]. This could be due to recalling previous injuries as well as the fear of recurrence of similar injuries in future earthquakes. This goes back to the culture of societies that their members don’t believe that they are at risk of the occurrence of hazards and their consequences until they experience these hazards. Regarding the high frequency of earthquakes in the Hamadan province, most of the participants in this study had previous earthquake experience but they were not prepared for earthquakes. Perhaps this is because most of the recent earthquakes in Hamadan did not result in deaths and as a result, these households do not take the risk of earthquakes seriously and do not find it essential to hold earthquake preparedness [ 28 ].

Besides, education was significantly correlated with households’ earthquake preparedness, which is similar to the results of the studies by Russell et al. and Ghadiri & Nasabi [ 29 , 30 ]. One explanation can be that people with higher education are more knowledgeable, more aware of earthquakes danger, and more inclined to acquire new skills [ 28 , 31 ].

In this study, we found that the preparedness of participants has a significant relationship with homeownership. Two previous studies showed homeowners were more prepared for earthquakes than renters [ 32 , 33 ], whereas a study in Ethiopia in 2014 showed that homeownership had no relationship with disaster preparedness [ 28 ]. One of the explanations is that owners can make the necessary changes despite preparedness costs due to place attachment, but more studies are required to confirm the role of homeownership.

We adjusted for multiple possibly confounding factors in our analysis. After adjusting the model, perceived benefit, cues to action, and self-efficacy had significant predictors of earthquake preparedness. It is more possible that people’s earthquake preparedness increases when they are aware of the benefits of earthquake preparedness. Furthermore, people with high self-efficacy feel they can prepare for earthquakes [ 34 ]. On the other hand, people may find the earthquake hazardous but if they feel enough confident to reduce damages of earthquakes, they will engage in preparedness. If people perceive the benefits of a healthy behavior higher than the barriers of it, they will engage in that healthy behavior. Therefore, people may perceive earthquakes as a high threat but it can be expected that higher perceived benefits and self-efficacy among them result in higher preparedness. One possible explanation is that the perceived benefits motivate people to perform a specific behavior and adopt an action [ 10 ]. Besides, the significant association of self-efficacy with preparedness at the household level for earthquakes could be explained by the positive and strong association of cues to actions with earthquake preparedness at the household level. Self-efficacy can be improved by observational learning, role modeling, and encouragement. Self-efficacy affects one’s efforts to change risk behavior and causes the continuation of one’s safe behavior despite obstacles that may decrease motivation [ 10 ]. Moreover, cues to action associated with earthquake preparedness [ 1 ]. Cues to action mention to influences of the social environment such as family, friends, and mass media. Mass media can play a vital role in educating the public about earthquake preparedness.

This study has several limitations. Firstly, using a self-reporting approach for data gathering, and secondly, due to the low number of relevant studies on earthquake preparedness based on behavioral change models, it was less possible to compare different studies with the findings of this study. Third, it should be noted that the results of this study can be generalized in the study population and setting, but for other settings it should be done with caution. Despite these limitations, this study had some strengths, we use a theoretical framework for identifying factors that influence earthquake preparedness with a large sample size. Also, the findings of this study are useful for emergency service providers, health authorities, and policymakers in designing and implementing earthquake preparedness programs. This research is also useful for researchers as it can be used as a basis for future researches. It is recommended to design and implement interventions to improve household preparedness for an earthquake based on self-efficacy, perceived benefits, and cues to action.

Households’ earthquake preparedness was insufficient and low. Controlling the damaging consequences of earthquakes is related to the preparedness for earthquakes and can prevent its devastating effects. Perceived benefits, cues to action, and self-efficacy had a significant relationship with earthquake preparedness. The possibility of people being more prepared is increased when they are aware of and understand properly the benefits of being prepared for earthquakes and other disasters. People with high self-efficacy also feel more empowered for taking better care of themselves and their families during disasters. Cues to action would also encourage earthquake preparedness. Since health centers and TV and radio programs were the primary sources of learning about earthquakes for the people, it is recommended that broadcasting provides related programs and educates people about earthquake preparedness. The predictors that were assessed in this study can be taken into account for designing and implementing proper interventions in this field.

Acknowledgments

The authors gratefully thank all of the participants in this study.

Abbreviations

Authors’ contributions.

MRM has made substantial contributions to the conception and design of the study, took responsibility for and coordinated the acquisition of data and contributed actively in the analysis of the data and the writing of the manuscript. FRS has made substantial contributions to the conception and design of the study, interpretation of the data, and writing up the manuscript. MS contributed to the design of the study and preparation of the manuscript. MRY was involved in the design of the study and the data gathering process. ADI contributed to the study design, data analysis, and interpretation. All authors read and approved the final manuscript.

This study was approved and financially supported by the Deputy of Research and Technology of Hamadan University of Medical Sciences (number: 9707174168). The funder of this study had no role in the study design, data collection, data analysis, data interpretation, or writing the manuscript.

Availability of data and materials

Ethics approval and consent to participate.

This study was approved by the Ethical Committee of Hamadan University of Medical Sciences (approval code: IR.UMSHA.REC.1397.359). This study was an observational questionnaire study and the anonymous questionnaires were used to collect data. Therefore, the verbal informed consent was obtained from all participants prior to participation in the study and filling out the questionnaires. The form of consent was approved by the ethics committee.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Dense network of seismometers reveals how the underground ruptures

by Helmholtz Association of German Research Centres

The idea that earthquakes release stress by a single strong quake along a single fault plane may need to be corrected. A recent study by researchers from the Karlsruhe Institute of Technology (KIT) with the participation of the GFZ German Research Centre for Geosciences and international partner institutions points out that it would be more accurate to speak of a zone with numerous fault planes, some of which are parallel.

According to the authors, the results of the study can help to create more realistic models for earthquakes and earthquake hazards in subduction zones. The study is published in the journal Nature .

The international team led by first author Caroline Chalumeau from KIT investigated a series of earthquakes in Ecuador on the west coast of South America. There, the Pacific Plate is subducted beneath the continental South American Plate. Subduction repeatedly leads to very severe earthquakes. The most recent series of earthquakes in Taiwan, the main quake of which killed nine people and caused extensive damage on Taiwan's east coast at the beginning of April, can also be attributed to subduction.

The series of earthquakes in Ecuador analyzed by the team began on 12 March 2022 and ended on 26 May 2022. The most severe quake (magnitude 5.8) occurred on March 27 and triggered many smaller aftershocks over a short period of time. A dense network of 100 seismometers was located in the region at this time. It had been set up for the offshore experiment "High-resolution imaging of the subduction fault in the Pedernales Earthquake Rupture zone" (HIPER for short).

With the extraordinarily detailed data from the HIPER experiment and using artificial intelligence , the researchers were able to map more than 1,500 earthquakes and their respective fault planes at a depth of 15 to 20 kilometers in very high resolution.

"We observed that the seismicity of earthquakes occurred in a primary region—the main earthquake, so to speak—and in a secondary region, i.e. the aftershocks," says first author Dr. Caroline Chalumeau from the Geophysical Institute (GPI) at KIT. "Within the primary region, we observed that the seismicity occurred on several different fault planes, often on top of each other. In some places, parallel seismically active planes occurred, in other places only single ones."

The parallelism of the quakes was not linked to a specific depth. "We have found indications that the previous idea that the stress is released by a single strong quake along a single fault plane could be a thing of the past," says Professor Andreas Rietbrock from the GPI. "Instead, we should rather speak of a fault network in which a series of ruptures discharges within a single earthquake."

The analysis of the Ecuadorian quake series also provides new insights into aftershocks. These first occurred near the epicenter of the main quake and then gradually spread in other directions, says Chalumeau. She concludes from this that the propagation of aftershocks in the region is mainly controlled by afterslip.

Prof. Onno Oncken from the GFZ says, "With this work, Caroline Chalumeau's team has presented the first sharp seismological image of a seismogenic plate boundary. On the one hand, it confirms existing geological observations and, on the other hand, successfully explains the propagation of aftershocks with a new approach. Previous assumptions that, for example, fluid diffusion causes aftershocks have thus been refuted."

The results are also important for assessing the earthquake risk in subduction zones . "The study will influence the future modeling of earthquakes, but also of aseismic slips, i.e. plate movements without earthquakes," says Rietbrock.

Journal information: Nature

Provided by Helmholtz Association of German Research Centres

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ENCYCLOPEDIC ENTRY

Earthquakes.

Earthquakes happen everyday around the world. Often, people can’t feel them, but sometimes they cause great devastation.

Earth Science, Geology

Woman Awaiting Aid after Earthquake

While most earthquakes are barely noticeable, others can be devastating. Major earthquakes—magnitude 7.0-7.9—and great earthquakes—magnitude of 8.0 or higher—can destroy buildings and kill people and other animals.

Photograph by ARKO DATTA/AFP

Hundreds of earthquakes occur on Earth everyday. Most of them are small, barely detectable by most people. But occasionally there is a much more significant quake. On average, a major earthquake —one with a magnitude of 7.0-7.9—strikes somewhere on the planet more than once a month. A great earthquake —with a magnitude of 8.0 or higher—occurs about once a year. An earthquake can happen anywhere. However, the vast majority of earthquakes occur at the boundaries between tectonic plates . Continental and oceanic plates may move toward each other, scrape past each other, or pull apart as they move slowly across the planet's upper mantle. This movement of the plates, and the pressure that is built up at the boundaries, can result in earthquakes . The boundaries between plates contains systems of deep cracks, called faults . Most earthquakes occur along these faults . Within a fault , rock masses on either side of the break are pushed by geologic forces in opposite directions. Friction , however, holds the rocks in place, causing stresses to build. Finally, the mounting pressure overcomes the friction and a sudden movement occurs along the fault , releasing a large amount of energy. This is an earthquake . While the vast majority of earthquakes occur along faults at Earth's plate boundaries, occasionally, a quake occurs in the middle of a plate, far from any boundary. Such quakes make up less than 10 percent of all earthquakes . While these intraplate quakes are not completely understood, scientists theorize that they may result from weaknesses within Earth’s crust from long ago. Although rare and not well understood, these earthquakes are no less devastating than those that occur along plate boundaries. Earthquakes along the New Madrid Fault , along the Mississippi River in the United States, in 1811–1812 were among the strongest quakes ever recorded. More recently, in 2001, an intraplate earthquake in the Gujarat region of northwesternn India killed more than 20,000 people. Believe it or not, earthquakes aren’t just Earth-bound phenomena. Astronauts who traveled to the moon in the late 1960s and early 1970s installed seismographs , devices used to measure and record vibrations, on the lunar surface. The data radioed back to Earth showed that “moonquakes” occur and can be fairly strong and last much longer than quakes on Earth!

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How an Earthquake Can Throw the Body and Brain Off-Balance

People can experience dizziness, anxiety and even “phantom” aftershocks following a quake.

By Erik Vance

• April 5, 2024

Earthquakes are always unnerving. But for some, the aftershocks can go on beyond the actual tremors: People can experience anxiety, sleep problems and other health issues in the hours and days after a quake.

One such effect is a sense of dizziness after an especially large or frightening earthquake. In Japan, this feeling is called jishin-yoi (which roughly translates to “earthquake drunk,” or “earthquake sickness”). It is also sometimes called post-earthquake dizziness syndrome . Others might report experiencing “phantom” earthquakes that might feel like subtle aftershocks, or like the room has started shaking again, but this is in fact purely psychological.

There is very little research into these phenomena, and most of it has been done in the wake of earthquakes far larger than the one that jolted the Northeast on Friday.

In Tokyo, where aftershocks are more common than in other parts of Japan, one team found that some people still experienced balance issues for as long as four months after a big quake.

“We see it with patients who get off cruises too, or get off a boat. They’ll be lightheaded or have a sensation of movement for days or even months,” said Dr. Landon Duyka, an ear, nose and throat surgeon at Northwestern Medicine.

If you are dizzy or feel like the ground is still moving after an earthquake ends, experts recommend treating it as you would other forms of motion sickness. Try looking at a spot far away and focusing on it, Dr. Duyka said, which “can often help what we call the vestibular system — or your balance system — settle down.”

If your dizzy spell doesn’t go away on its own within a few hours, or if it is particularly intense, you may want to look into over-the-counter antihistamines, like Dramamine, Dr. Duyka said.

Some feelings may be caused more by stress. Experts said that it’s normal to feel anxiety, especially if you’ve never experienced an earthquake before.

You can’t control earthquakes, said Susan Albers, a clinical psychologist at the Cleveland Clinic who has worked with patients who have weather-related fears. “That’s where it really taps into people’s anxiety, and particularly if you’re somebody who already has issues with control.”

Dr. Albers said it’s important to avoid “doom-scrolling” after experiencing a stressful event like an earthquake. If you feel compelled to read about it, she recommended focusing on scientific explanations of earthquakes and how they work, rather than the destruction they cause. This is especially helpful for children, Dr. Albers added.

She also recommended sharing your experience with people around you, talking about where you were and what it felt like. Seek out people who project a sense of ease about the event, Dr. Albers said — or, if you are able, become that person for others.

“Being around people who are calm about the situation can be really helpful,” she said. “Calm is contagious.”

Erik Vance is a staff editor for The Times’s Well desk, where he focuses on coverage of fitness and a healthy lifestyle. More about Erik Vance

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A strong earthquake in Japan leaves 9 people with minor injuries. But there was no tsunami danger

Police officers clean the debris from an earthquake in Uwajima, Ehime prefecture, western Japan Thursday, April 18, 2024. According to Kyodo News reports, a strong earthquake hit Ehime and Kochi prefectures in western Japan on Wednesday night, but no tsunami warning was issued. (Kyodo News via AP)

This shows the site of a rock fall following an earthquake in Ohzu, Ehime prefecture, western Japan Thursday, April 18, 2024. According to Kyodo News reports, a strong earthquake hit Ehime and Kochi prefectures in western Japan on Wednesday night, but no tsunami warning was issued. (Kyodo News via AP)

• Copy Link copied

TOKYO (AP) — A strong earthquake that struck southwestern Japan left nine people with minor injuries and caused damage such as burst water pipes and small landslides, authorities said Thursday. But there was no danger of a tsunami.

The magnitude 6.6 temblor late Wednesday was centered just off the western coast of the southwestern main island of Shikoku, in an area called the Bungo Channel, a strait separating Shikoku and the southern main island of Kyushu.

The quake occurred about 50 kilometers (30 miles) below the sea’s surface and posed no danger of a tsunami, the Japanese Meteorological Agency said.

The Fire and Disaster Management Agency said Thursday that six in Ehime prefecture, one in neighboring Kochi and two others in Oita on Kyushu island suffered minor injuries, mostly from falling at home.

Water pipes were ruptured at a number of locations in Sukumo City in Kochi prefecture, and grave stones collapsed at a Buddhist temple in Ainan town in Ehime prefecture, according to local media reports. Falling rooftiles were also reported.

The Nuclear Regulation Authority said that no abnormalities were reported from four reactors operating at three nuclear power plants in Shikoku and Kyushu.

As part of the Pacific “ring of fire,” Japan is one of the world’s most earthquake-prone areas. A magnitude 9.0 earthquake and subsequent tsunami in March 2011 devastated large areas along Japan’s northeastern coast, killing nearly 20,000 people and triggering the Fukushima Daiichi nuclear meltdowns. On Jan. 1, a magnitude 7.6 quake struck the north-central region of Noto and left 241 people dead.

This story corrects the spelling of the last name in the byline to Yamaguchi, not Yamaguchia, and to show that the number of injured in Kochi was one, not two.

• Research article
• Open access
• Published: 08 May 2020

Earthquake preparedness of households and its predictors based on health belief model

• Masoumeh Rostami-Moez 1 , 2 ,
• Mohammad Rabiee-Yeganeh 2 ,
• Mohammadreza Shokouhi 3 ,
• Amin Dosti-Irani 1 , 4 &
• Forouzan Rezapur-Shahkolai   ORCID: orcid.org/0000-0001-5049-1109 5 , 6  

BMC Public Health volume  20 , Article number:  646 ( 2020 ) Cite this article

22k Accesses

26 Citations

Metrics details

Earthquakes are one of the most destructive natural disasters in which many people are injured, disabled, or died. Iran has only 1 % of the world’s population, but the percentage of its earthquake-related deaths is absolutely higher. Therefore, this study aimed to determine the level of earthquake preparedness of households and its predictors using the Health Belief Model (HBM).

This observational descriptive and analytical study was conducted on 933 households in Hamadan province, located in the west of Iran, in 2019. Multi-stage cluster random sampling was used for selecting the participants. The inclusion criteria were being at least 18 years old and being able to answer the questions. A questionnaire was used for data collection including earthquake preparedness, awareness of earthquake response, predictors of earthquake preparedness based on the HBM, and demographic information. Analysis of variance, independent t-test, and a linear regression model was used.

The mean age of participants was 38.24 ± 12.85 years. The average score of earthquake preparedness was low (approximately 30%). There was a significant relationship between earthquake preparedness and gender ( P  < 0.001), homeownership ( P  < 0.001), marriage status ( P  < 0.001), education ( P  < 0.001), and previous earthquake experience ( P  < 0.001). Regarding the HBM constructs, perceived benefits ( P  < 0.001), cues to action ( P  < 0.001), and self-efficacy ( P  < 0.001) were significant predictors of earthquake preparedness.

Conclusions

Earthquake preparedness was insufficient. Besides, perceived benefits, cues to action, and self-efficacy were predictors of earthquake preparedness. These predictors can be taken into account, for designing and implementing related future interventions.

Peer Review reports

Earthquakes are one of the most dangerous natural hazards that occur suddenly and uncontrollably. They cause physical, psychological, and social damages in human societies [ 1 ]. Over the past two decades, 800 million people have been injured by natural disasters. Besides, natural disasters have caused 42 million deaths in the world [ 2 ]. Iran is always at risk of earthquakes due to its geographical location on the Alpine-Himalayan orogenic belt [ 3 , 4 ]. More than 70% of the major cities in Iran are vulnerable to substantial damages. The earthquakes of recent decades have not only caused the deaths of thousands but also have caused massive economic damage and destroyed many cities and villages in the world [ 5 , 6 ]. Iran has only 1 % of the world’s population, but the percentage of its earthquake-related deaths is absolutely higher [ 7 ]. The disaster management cycle has four phases including mitigation, preparedness, response, and recovery. Preparedness is the most important phase in the disaster management cycle. Previous research in Iran has shown that the role of people as the most important and largest group has often been neglected in disaster preparedness program planning [ 8 ].

The Health Belief Model (HBM) describes the decision-making process that individuals use to adopt healthy behavior. It can be an effective framework for developing health promotion strategies [ 9 ]. Theoretically, in the HBM, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (the beliefs of individuals in their ability to prepare for disaster) predict behavior [ 1 , 9 , 10 ].

There are some studies on earthquake preparedness that have assessed the readiness of individuals based on their knowledge and skills [ 11 , 12 , 13 , 14 , 15 ]. Some studies have also considered structural and non-structural safety in some cities [ 16 ] and some studies have investigated students’ readiness [ 17 , 18 ]. There are a few studies that have used behavioral change models in the disaster area [ 5 ]. The Haraoka and Inal used the Health Belief Model to develop a questionnaire for earthquake preparedness [ 1 , 11 ].

Previous studies in Iran showed that most households did not have enough readiness and had a relatively high vulnerability to possible earthquake hazards [ 19 , 20 ]. Also, one study showed that improving the socio-economic status was correlated with improving the attitude of people about disaster preparedness [ 13 ]. In DeYoung et al.ʼs study, earthquake readiness was positively correlated with risk perception, self-efficacy, and trust in information about hazards through media [ 21 ].

To the best of the authors’ knowledge, this is the first study in Iran that examines earthquake preparedness of households, using a behavior change model. Considering the importance of earthquake preparedness of households, this study aims to asses the level of earthquake preparedness of households and its predictors based on HBM.

Study design and participants

This observational descriptive and analytical study was carried out in all counties of Hamadan province, located in the west of Iran, in 2019. These counties includes Hamadan (the capital of Hamadan province), Malayer, Tuyserkan, Nahavand, Razan, Bahar, Kabudarahang, Asadabad, and Famenin. Based on the previous study [ 19 ], the estimated sample size was 600 households. Cluster sampling was used for this study and we used the design effect of 1.5 plus 10% attrition. Subsequently, the final sample size was calculated at 1000 households. The data were collected from February to July 2019. From each county, a university graduate person was recruited and trained for data collection. The supervision and training were done by the first author. The verbal informed consent was obtained from all participants before the data gathering. The participants were first provided a description of the study and they were informed that the participation in the study was voluntary, and all study data were anonymous and confidential. Then, if they gave verbal informed consent, they would participate in the study and fill out the anonymous questionnaires. A person aged 18 or above was randomly selected from each household and answered the questions. For illiterate people, questionnaires were filled out through interviewing them. The inclusion criteria were being at least 18 years old and being able to answer the questions. The exclusion criteria were an incomplete questionnaire.

Participants have been selected by multi-stage cluster random sampling. First, stratified sampling was used for each county based on its urban and rural populations. Then, in urban and rural areas, a list of urban or rural health centers was listed and one health center was randomly selected in each county. After that, from the list of all households covered by the selected health center, one household was selected by simple random sampling and sampling started taking the clockwise direction of the selected household and continued until the required sample was collected. For selecting the sample of the urban population of Hamadan County, we selected one health center from each district by simple random sampling (in Hamadan city, there are four districts). In the next stage, from the list of covered households, one household was randomly selected and the sampling was started taking the clockwise direction until the required sample in each district was collected.

Measurements

The questionnaire used for data collection comprises four domains including 1) demographics, 2) earthquake preparedness 3) awareness on earthquake response, and 4) predictor of earthquake preparedness based on the HBM. Earthquake preparedness was response variable.

Demographics included age, sex, occupation, education, economic status, family size, number of individuals over 60 years old and under 16, earthquake experience, homeownership, marital status, and having a person with a disease that needs medication at their home.

We measured earthquake preparedness by an earthquake preparedness checklist [ 22 ]. This checklist was developed and validated by Spittal et al., in 2006. It consists of 23 questions with yes or no answers. The questions are about: having a working torch (flashlight), a first aid kit, a working battery radio, a working fire extinguisher, etc. [ 22 ]. We adapted this checklist by adding two items according to the context of the study. These two questions were: 1) do you know the necessary contact numbers such as fire station, police, and emergency so that you will be able to call them if needed?; 2) are you familiar with the phrase, “Drop, Cover, and Hold”? Also, we adapted it with some minor changes. We added “have learned first aid” to “have purchased first aid kit” statement. We added “and extra cloths and blankets” at the end of” put aside extra plastic bags and toilet paper for use as an emergency toilet” statement. We replaced “roof” with “my way” in “ensuring that the roof will probably not collapse in an earthquake. We added some examples to “take some steps at work” statement such as attending an earthquake preparedness class and having fire insurance. The content validity of the Persian checklist was tested by 10 experts. We calculated CVI and CVR equal to 0.92 and 0.95, respectively. Also, the face validity and reliability of this checklist were examined in a pilot study on 40 adults. According to their recommendations, minor revisions were made to increase the transparency and understandability of the statements. Likewise, the reliability of this checklist was measured by internal consistency (Chronbach α = 0.858). The total score of this checklist was ranging from 0 to 25 and the higher score reflects more preparedness.

The awareness on earthquake response questionnaire included seven questions with true/false answers (In an earthquake: you should get down close to the ground; you should get under a big piece of furniture such as a desk or other covers; you should hold on to a firm object until the end of the shaking; you should stand in a doorway; If you are indoors during an earthquake, you must exit the building; If you are in bed during an earthquake, you should stay there and cover your head with a pillow; next to pillars of buildings and interior wall corners are the safe areas). One point was given for each correct answer. Therefore, the total score of this domain was seven points.

The adapted questionnaire of earthquake preparedness based on the HBM was used. The original questionnaire has been established and validated by Inal et al. [ 1 ] in Turkey. The forward and backward translation method was used for translating the original questionnaire. According to the experts’ opinions, some minor changes were made to adapt the items of the questionnaire for the study population in the present study. Thereby, three questions were added to the questions of the cues to action (Radio and TV encourage me to prepare for disasters, I usually seek information about disaster preparedness from Radio and TV, and I usually obtain information about disaster preparedness from health providers). Besides, one question was added to the questions of perceived benefits (preparedness for disaster will reduce financial losses and injuries). Then, the content validity of the questionnaire was assessed by a panel of experts including 10 Health specialists in the field of health in disasters, health education, health promotion, and safety promotion (CVR = 0.92 & CVI = 0.85). Next, the face validity and reliability of the questionnaire were measured in a pilot study on 40 people over 18 years old. The reliability was calculated by using internal consistency. One question from the perceived severity (emergency and the experience of disasters does not change my life) and one question from self-efficacy (I cannot create an emergency plan with my neighbors) was excluded based on the results of Cronbach’s alpha. In Iran, neighbors don’t share their plans; therefore, it was logical to exclude these items. Finally, the questionnaire consisted of 33 questions, including perceived severity (2 questions, α = 0.709), perceived susceptibility (6 questions, α = 0.664), perceived benefits (4 questions, α = 0.758), perceived barriers (6 questions, α = 0.822), self-efficacy (7 questions, α = 0.677), cues to action (8 questions, α = 0.683), and total questions (33 questions, α = 0.809). All of the items were assessed by a 5-point Likert scale ranging from ‘completely disagree’ (one point) to ‘completely agree’ (5 points). Some items were scored reversely.

Statistical analysis

We used the analysis of variance (ANOVA) and independent t-test to determine the relationship between variables. Besides, the multivariate linear regression model was used to determine the predictors of household earthquake preparedness. The Stata 14.2 software was used to analyze the data.

In this study, 933 questionnaires were analyzed (response rate: 93.3%). The mean age of participants was 38.24 ± 12.85 years. Besides, 228 (24.44%) participants were male and 656 (70.31%) were female. About 80% of the participants did not have an academic education and had a diploma degree or less than a diploma degree. Also, 573 (61.41%) participants were homeowners (Table  1 ).

The earthquake preparedness of the participants was low. The household preparedness score was 7.5 out of 25. In other words, the average earthquake preparedness of households was approximately 30%. Besides, the self-efficacy score was 60.79 ± 0.55 and the score of cues to action was 66.57 ± 0.45 (Table  2 ).

The participants’ preparedness for the earthquake had a significant relationship with gender ( P  < 0.001), homeownership ( P  < 0.001), marital status ( P  < 0.001), and previous experience of a destructive earthquake ( P  < 0.001). Also, the mean score of earthquake preparedness was higher in those who reported moderate or good economic status. The mean difference was statistically significant by the Scheffe test ( P  < 0.001). Furthermore, the one-way ANOVA/Scheffe’s test showed that there was a significant difference between illiterate people and those who had either university education or diploma degree and similarly, a significant difference in earthquake preparedness was observed between primary education and those who had either academic education or diploma degree ( P  < 0.001) (Table  3 ).

The crude regression analysis showed that all constructs of the HBM except perceived severity were significant predictors of earthquake preparedness (P < 0.001) but after using stepwise regression, only perceived benefits ( P  < 0.006), cues to action ( P  < 0.001), and self-efficacy ( P  < 0.001), significantly predicted the earthquake preparedness (Table  4 ).

In this study, we determined the level of earthquake preparedness of households and its predictors based on HBM. The earthquake preparedness of the participants was low. The participants’ preparedness for the earthquake had a significant relationship with homeownership, education, and previous experience of a destructive earthquake. Also, perceived benefits, cues to action, and self-efficacy significantly predicted the earthquake preparedness.

Despite the strong emphasis on earthquake preparedness to prevent its damaging effects, the findings of this study showed that most people had low preparedness for earthquakes which is similar to the findings of previous studies [ 18 , 23 , 24 , 25 ]. This can be very dangerous in areas that are vulnerable to earthquakes. Earthquake preparedness is related to the previous experience of destructive earthquakes and their damaging consequences. Households that had previously experienced destructive earthquakes were more prepared than those who had not previously experienced this event, which is similar to previous finding [ 26 , 27 ]. People who live in earthquakes zones and understand the potential losses from earthquakes are more likely to be prepared in comparison to people living in other areas [ 18 ]. This could be due to recalling previous injuries as well as the fear of recurrence of similar injuries in future earthquakes. This goes back to the culture of societies that their members don’t believe that they are at risk of the occurrence of hazards and their consequences until they experience these hazards. Regarding the high frequency of earthquakes in the Hamadan province, most of the participants in this study had previous earthquake experience but they were not prepared for earthquakes. Perhaps this is because most of the recent earthquakes in Hamadan did not result in deaths and as a result, these households do not take the risk of earthquakes seriously and do not find it essential to hold earthquake preparedness [ 28 ].

Besides, education was significantly correlated with households’ earthquake preparedness, which is similar to the results of the studies by Russell et al. and Ghadiri & Nasabi [ 29 , 30 ]. One explanation can be that people with higher education are more knowledgeable, more aware of earthquakes danger, and more inclined to acquire new skills [ 28 , 31 ].

In this study, we found that the preparedness of participants has a significant relationship with homeownership. Two previous studies showed homeowners were more prepared for earthquakes than renters [ 32 , 33 ], whereas a study in Ethiopia in 2014 showed that homeownership had no relationship with disaster preparedness [ 28 ]. One of the explanations is that owners can make the necessary changes despite preparedness costs due to place attachment, but more studies are required to confirm the role of homeownership.

We adjusted for multiple possibly confounding factors in our analysis. After adjusting the model, perceived benefit, cues to action, and self-efficacy had significant predictors of earthquake preparedness. It is more possible that people’s earthquake preparedness increases when they are aware of the benefits of earthquake preparedness. Furthermore, people with high self-efficacy feel they can prepare for earthquakes [ 34 ]. On the other hand, people may find the earthquake hazardous but if they feel enough confident to reduce damages of earthquakes, they will engage in preparedness. If people perceive the benefits of a healthy behavior higher than the barriers of it, they will engage in that healthy behavior. Therefore, people may perceive earthquakes as a high threat but it can be expected that higher perceived benefits and self-efficacy among them result in higher preparedness. One possible explanation is that the perceived benefits motivate people to perform a specific behavior and adopt an action [ 10 ]. Besides, the significant association of self-efficacy with preparedness at the household level for earthquakes could be explained by the positive and strong association of cues to actions with earthquake preparedness at the household level. Self-efficacy can be improved by observational learning, role modeling, and encouragement. Self-efficacy affects one’s efforts to change risk behavior and causes the continuation of one’s safe behavior despite obstacles that may decrease motivation [ 10 ]. Moreover, cues to action associated with earthquake preparedness [ 1 ]. Cues to action mention to influences of the social environment such as family, friends, and mass media. Mass media can play a vital role in educating the public about earthquake preparedness.

This study has several limitations. Firstly, using a self-reporting approach for data gathering, and secondly, due to the low number of relevant studies on earthquake preparedness based on behavioral change models, it was less possible to compare different studies with the findings of this study. Third, it should be noted that the results of this study can be generalized in the study population and setting, but for other settings it should be done with caution. Despite these limitations, this study had some strengths, we use a theoretical framework for identifying factors that influence earthquake preparedness with a large sample size. Also, the findings of this study are useful for emergency service providers, health authorities, and policymakers in designing and implementing earthquake preparedness programs. This research is also useful for researchers as it can be used as a basis for future researches. It is recommended to design and implement interventions to improve household preparedness for an earthquake based on self-efficacy, perceived benefits, and cues to action.

Households’ earthquake preparedness was insufficient and low. Controlling the damaging consequences of earthquakes is related to the preparedness for earthquakes and can prevent its devastating effects. Perceived benefits, cues to action, and self-efficacy had a significant relationship with earthquake preparedness. The possibility of people being more prepared is increased when they are aware of and understand properly the benefits of being prepared for earthquakes and other disasters. People with high self-efficacy also feel more empowered for taking better care of themselves and their families during disasters. Cues to action would also encourage earthquake preparedness. Since health centers and TV and radio programs were the primary sources of learning about earthquakes for the people, it is recommended that broadcasting provides related programs and educates people about earthquake preparedness. The predictors that were assessed in this study can be taken into account for designing and implementing proper interventions in this field.

Availability of data and materials

The analyzed datasets during this study are available from the corresponding author on reasonable request.

Abbreviations

Health Belief Model

Confidence Interval

Analysis of Variance

Content Validity Ratio

Content Validity Index

Inal E, Altintas KH, Dogan N. The development of a general disaster preparedness belief scale using the health belief model as a theoretical framework. Int J Assess Tools Educ. 2018;5(1):146–58.

Google Scholar  

Şakiroğlu M. Variables related to earthquake preparedness behavior. Ankara: Unpublished MS Thesis, Department of Psychology, Middle East Technical University; 2005.

Walters RJ, England P, Houseman G. The dynamics of continental convergence in Iran. In: AGU Fall Meeting [Abstracts]; 2016.

Bakhtiari A. Country report: the Islamic Republic of Iran on disaster risk management. Kobe: Iranian National Disaster Management Organization; 2014. (in Persian).

Ejeta LT, Ardalan A, Paton D. Application of behavioral theories to disaster and emergency health preparedness: a systematic review. PLoS Curr. 2015;7. https://doi.org/10.1371/currents.dis.31a8995ced321301466db400f1357829 .

Abdolizadeh S, Maleki Z, Arian M. Earthquake hazard zonation and seismotectonics of the Bandar Abbas area, Zagros, Iran. Open J Geol. 2016;6(3):210–24.

Article   Google Scholar  

Building and Housing Research Center. Iranian code of practice for seismic resistant design of buildings. Standard No. 2800. 2005. (In Persian). Available from: http://des.sutech.ac.ir/sites/omrani.sutech.ac.ir/files/Groups/client/www.tanbakoochi.com-2800-V4.pdf .

Farajzadeh M, Ghanei Gheshlagh R, Beiramijam M, Dalvand S, Ghawsi S, Amini H. Preparedness of nurses for crises and disasters in imam Khomeini and social security hospitals of Saqqez. Health Emerg Disaster Quart. 2017;3(1):57–63.

Teitler-Regev S, Shahrabani S, Benzion U. Factors affecting intention among students to be vaccinated against a/H1N1 influenza: a health belief model approach. Adv Prev Med. 2011. https://doi.org/10.4061/2011/353207 .

Glanz K, Rimer BK, Viswanath K. Health behavior and health education: theory, research, and practice. San Francisco: Wiley; 2008.

Haraoka T, Ojima T, Murata C, Hayasaka S. Factors influencing collaborative activities between non-professional disaster volunteers and victims of earthquake disasters. PLoS One. 2012;7(10):e47203.

Article   CAS   Google Scholar  

Cvetković V, Dragičević S, Petrović M, Mijalković S, Jakovljević V, Gačić J. Knowledge and perception of secondary school students in Belgrade about earthquakes as natural disasters. Pol J Environ Stud. 2015;24(4):1553–61.

Rezaee M, Lashgari E, Nori M. Evaluation disaster management status of Kerman city with emphasis on mental–attitude preparedness. Hum Geogr Res. 2018;50(4):853–71 (in Persian).

Baytiyeh H, Öcal A. High school students’ perceptions of earthquake disaster: a comparative study of Lebanon and Turkey. Int J Disaster Risk Reduct. 2016;18:56–63.

Adem Ö. The relationship between earthquake knowledge and earthquake attitudes of disaster relief staffs. Disaster Adv. 2011;4(1):19–24.

Dargahi A, Farrokhi M, Poursadeghiyan M, Ahagh M, Karami A. Evaluation of functional preparedness and non structural safety of different health units of Kermanshah University of Medical Sciences in coping with natural disas-ters. Health Emerg Disaster Quart. 2017;2(4):201–6.

Ronan KR, Alisic E, Towers B, Johnson VA, Johnston DM. Disaster preparedness for children and families: a critical review. Curr Psychiatry Rep. 2015;17(7):58.

Amanat N, Khankeh H, Hosseini MA, Mohammadi F, Sadeghi A, Aghighi A. The effect of earthquake preparedness training to male high school students on families’ preparedness in Eshtehard city in 2010-2011. J Rescue Relief. 2013;5(3):27–39.

Rakhshani T, Abbasi S, Ebrahimi M, Travatmanesh S. Investigating the preparedness status of households against earthquake in Fars Province in 2013; a cross sectional study. Iranian J Emerg Med. 2016;3(2):66–72.

Jamshidi E, Majdzadeh R, Namin MS, Ardalan A, Majdzadeh B, Seydali E. Effectiveness of community participation in earthquake preparedness: a community-based participatory intervention study of Tehran. Disaster Med Public Health Prep. 2016;10(2):211–8.

DeYoung SE. Disaster preparedness: psychosocial predictors for Hazard readiness. Raleigh: PhD thesis; 2014.

Spittal MJ, Walkey FH, McClure J, Siegert RJ, Ballantyne KE. The earthquake readiness scale: the development of a valid and reliable unifactorial measure. Nat Hazards. 2006;39(1):15–29.

Rañeses MK, Chang-Richards A, Richards J, Bubb J. Measuring the level of disaster preparedness in Auckland. Procedia Eng. 2018;212:419–26.

Ozmen F. The level of preparedness of the schools for disasters from the aspect of the school principals. Disaster Prev Manag. 2006;15(3):383–95.

Wu G, Han Z, Xu W, Gong Y. Mapping individuals’ earthquake preparedness in China. Nat Hazards Earth Syst Sci. 2018;18(5):1315–25.

Shapira S, Aharonson-Daniel L, Bar-Dayan Y. Anticipated behavioral response patterns to an earthquake: the role of personal and household characteristics, risk perception, previous experience and preparedness. Int J Disaster Risk Reduct. 2018;31:1–8.

Najafi M, Ardalan A, Akbarisari A, Noorbala AA, Jabbari H. Demographic determinants of disaster preparedness behaviors amongst Tehran inhabitants, Iran. PLoS Curr. 2015;7. https://doi.org/10.1371/currents.dis.976b0ab9c9d9941cbbae3775a6c5fbe6 .

Ashenefe B, Wubshet M, Shimeka A. Household flood preparedness and associated factors in the flood-prone community of Dembia district, Amhara National Regional State, Northwest Ethiopia. Risk Manag Healthc Policy. 2017;10:95–106.

Russell LA, Goltz JD, Bourque LB. Preparedness and hazard mitigation actions before and after two earthquakes. Environ Behav. 1995;27(6):744–70.

Ghadiri M, Nasabi N. Analysis of the difference of mental-attitude preparedness of earthquake in shiraz households. Geogr Urban Plan Res. 2015;3(2):227–45 (in Persian).

Edwards ML. Social location and self-protective behavior: implications for earthquake preparedness. Int J Mass Emerg Disasters. 1993;11(3):293–303.

Oral M, Yenel A, Oral E, Aydin N, Tuncay T. Earthquake experience and preparedness in Turkey. Disaster Prev Manag. 2015;24(1):21–37.

Kirschenbaum A. Families and disaster behavior: a reassessment of family preparedness. Int J Mass Emerg Disasters. 2006;24(1):111–43.

Paton D. Disaster preparedness: a social-cognitive perspective. Disaster Prev Manag. 2003;12(3):210–6.

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Acknowledgments

The authors gratefully thank all of the participants in this study.

This study was approved and financially supported by the Deputy of Research and Technology of Hamadan University of Medical Sciences (number: 9707174168). The funder of this study had no role in the study design, data collection, data analysis, data interpretation, or writing the manuscript.

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Masoumeh Rostami-Moez & Mohammad Rabiee-Yeganeh

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MRM has made substantial contributions to the conception and design of the study, took responsibility for and coordinated the acquisition of data and contributed actively in the analysis of the data and the writing of the manuscript. FRS has made substantial contributions to the conception and design of the study, interpretation of the data, and writing up the manuscript. MS contributed to the design of the study and preparation of the manuscript. MRY was involved in the design of the study and the data gathering process. ADI contributed to the study design, data analysis, and interpretation. All authors read and approved the final manuscript.

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This study was approved by the Ethical Committee of Hamadan University of Medical Sciences (approval code: IR.UMSHA.REC.1397.359). This study was an observational questionnaire study and the anonymous questionnaires were used to collect data. Therefore, the verbal informed consent was obtained from all participants prior to participation in the study and filling out the questionnaires. The form of consent was approved by the ethics committee.

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Rostami-Moez, M., Rabiee-Yeganeh, M., Shokouhi, M. et al. Earthquake preparedness of households and its predictors based on health belief model. BMC Public Health 20 , 646 (2020). https://doi.org/10.1186/s12889-020-08814-2

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Nalbant, S. S., McCloskey, J., Steacy, S. & Barka, A. A. Earth Planet. Sci. Lett. 195 , 291–298 (2002).

Article   Google Scholar  

Stein, R. S., Barka, A. A. & Dieterich, J. H. Geophys. J. Int. 128 , 594–604 (1997).

McCloskey, J., Nalbant, S. & Steacy, S. Nature 434 , 291 (2005).

Article   PubMed   Google Scholar  

Toda, S., Lin, J., Meghraoui, M. & Stein, R. S. Geophys. Res. Lett. 35 , L17305 (2008).

Parsons, T. Geophys. Res. Lett. 107 , 2199 (2002).

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Stein, R. Nature 402 , 605–609 (1999).

Barka, A. Science 285 , 1858–1859 (1999).

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