earthquake research report

U.S. Government Accountability Office

Earthquakes: Opportunities Exist to Further Assess Risk, Build Resilience, and Communicate Research

The National Earthquake Hazards Reduction Program helps U.S. communities strengthen their earthquake resilience. For example, the program educates the public on earthquake risks and helps communities update building codes and improve design and construction practices.

The program has started assessing progress, for example, by tracking building code adoption in 22,000 jurisdictions. But we found that a national assessment hasn't been done. Such an assessment could help the program more strategically address inconsistencies in how states, localities, territories, and tribes mitigate earthquake risks.

Our recommendations address this, and more.

Damage from the 2020 Southwest Puerto Rico Earthquake Sequence

What GAO Found

The National Earthquake Hazards Reduction Program (NEHRP) has goals outlined in its most recent Strategic Plan for fiscal years (FY) 2009-2013 for the improvement of earthquake resilience in communities nationwide. However, officials from the National Institute of Science and Technology (NIST) said that a national risk assessment has not been done to identify improvements and remaining gaps in resilience. The Federal Emergency Management Agency (FEMA) initiated some efforts to identify improvements by collecting data on the adoption of building codes. The NEHRP agencies are currently working to update the Strategic Plan FY 2022-2029. By conducting a national risk assessment, NEHRP would gain greater awareness of earthquake resilience improvements and be better positioned in planning long-term goals and objectives toward closing remaining gaps.

Damage to Anchorage, Alaska Following a Magnitude 7.1 Earthquake in 2018

Accomplishing NEHRP's strategic objectives requires developing and applying research in the geological, engineering, and social sciences areas. NEHRP identifies research priorities, and many of the NEHRP agencies award grants to entities such as universities or state and local agencies, to conduct research. While the communication mechanisms used by the National Science Foundation (NSF) include program solicitations, program descriptions, and letters issued to research entities, they do not communicate NEHRP's strategic research priorities. By developing strategies to better communicate its research priorities, NEHRP can help ensure that they are met.

NEHRP's Program Coordination Working Group is responsible for coordinating the implementation of NEHRP's strategic research priorities and has followed leading practices for leadership and outcomes. However, the working group did not follow two leading practices for accountability and resources. For example, the working group did not track and monitor progress, and did not identify and leverage resources needed to achieve outcomes for research priorities. Identifying resources would enable the interagency group to leverage all relevant resources across the NEHRP agencies, and better align them with research priorities. Further, the identification of resources would provide an opportunity for the working group to build programmatic partnerships aimed at strengthening earthquake resilience.

Why GAO Did This Study

Established in 1977, NEHRP aims to help reduce the risks to life and property from earthquakes. NEHRP's initiatives include strengthening community resilience through improved design and construction methods, conducting research to better understand the impacts from earthquakes, and providing outreach and education. NEHRP is comprised of four federal agencies (FEMA, NIST, NSF, and the U.S. Geological Survey) that promote and support NEHRP's initiatives for strengthening earthquake resilience.

The National Earthquake Hazards Reduction Program Reauthorization Act of 2018 includes a provision for GAO to assess the program's efforts. This report examines, among other things, NEHRP's progress in identifying gaps and strengthening resilience to earthquakes, and its activities to identify and communicate about research priorities. GAO reviewed NEHRP's strategic plans, agency guidance, and external communications; compared procedures to leading practices for interagency collaboration; and interviewed federal and state officials, among others.

Recommendations

GAO is making seven recommendations, including that NEHRP agencies conduct a national assessment to identify progress and remaining gaps in earthquake resilience; develop strategies to better communicate research priorities; and follow leading practices to identify and leverage resources. NIST, NSF, and FEMA concurred with our recommendations.

Recommendations for Executive Action

Full report, gao contacts.

Christopher P. Currie Director [email protected] (404) 679-1875

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• Published: 06 August 2021

Machine learning and earthquake forecasting—next steps

• Gregory C. Beroza 1 ,
• Margarita Segou 2 &
• S. Mostafa Mousavi 1  

Nature Communications volume  12 , Article number:  4761 ( 2021 ) Cite this article

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A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving earthquake forecasting.

The past 5 years have seen a rapidly accelerating effort in applying machine learning to seismological problems. The serial components of earthquake monitoring workflows include: detection, arrival time measurement, phase association, location, and characterization. All of these tasks have seen rapid progress due to effective implementation of machine-learning approaches. They have proven opportune targets for machine learning in seismology mainly due to the large, labeled data sets, which are often publicly available, and that were constructed through decades of dedicated work by skilled analysts. These are the essential ingredient for building complex supervised models. Progress has been realized in research mode to analyze the details of seismicity well after the earthquakes being studied have occurred, and machine-learning techniques are poised to be implemented in operational mode for real-time monitoring. We will soon have a next generation of earthquake catalogs that contain much more information. How much more? These more complete catalogs typically feature at least a factor of ten more earthquakes (Fig.  1 ) and provide a higher-resolution picture of seismically active faults.

a Real-time catalog, available at http://cnt.rm.ingv.it/ and ( b ) machine-learning catalog 16 are shown for event magnitudes above their respective magnitude of completeness 12 , 16 Mc = 2.2 and Mc = 0.5.

This next generation of earthquake catalogs will not be the single, static objects seismologists are accustomed to working with. For example, less than 2 years after the 2019 Ridgecrest, California earthquake sequence there already exist four next-generation catalogs, each of which were developed with different enhanced detection techniques. Now, and in the future, this will be the norm, and earthquake catalogs will be updated and improved—potentially dramatically—with time. Second-generation deep learning models 1 that are specifically designed based on earthquake signal characteristics and that mimic the manual processing by analysts, can lead to performance increases beyond those offered by earlier models that adapted neural network architectures from other fields. Those interested in using earthquake catalogs for forecasting can anticipate a shifting landscape with continuing improvements.

While these improvements are impressive, the value of the extra information they provide is less clear. What will we learn about earthquake behavior from these deeper catalogs and how might it improve the prospects for the stubbornly difficult problem of earthquake forecasting?

Short-term deterministic earthquake prediction remains elusive and is perhaps impossible; however, probabilistic earthquake forecasting is another matter. It remains the subject of focused and sustained attention and it informs earthquake hazard characterization 2 and thus both policy and earthquake risk reduction. A key assumption is that what we learn from the newly uncovered small earthquakes in AI-based catalogs, will inform earthquake forecasting for events of all magnitudes. The observed scale invariance of earthquake behavior suggests this is a reasonable expectation.

Empirical seismological relationships have played a key role in the development of earthquake forecasting. These include Omori’s law 3 that describes the temporal decay of aftershock rate, the magnitude-frequency distribution, with the b-value describing the relative numbers of small vs. large earthquakes 4 , and the Epidemic Type Aftershock Sequence (ETAS) model 5 in which earthquakes are treated as a self-exciting process governed by Omori’s law for their frequency of occurrence and Gutenberg–Richter statistics for their magnitude. These empirical laws continue to prove their utility. Just in the past few years, the time dependence of the b-value has been used to try to anticipate the likelihood of large earthquakes during an ongoing earthquake sequence 6 and the ETAS model has been improved to better anticipate future large events 7 . So it appears that there is value in applying these longstanding relationships to improved earthquake catalogs, but our opinion is that much more needs to be done.

The relationships cited above date from 127, 77, and 33 years ago. The oldest of them, Omori’s Law, was developed based on felt reports without the benefit of instrumental measurements. We suggest that a fresh approach using more powerful techniques is warranted. Earthquake catalogs are complex, high-dimensional objects and as Fig.  1 makes clear, that is even more true for the deeper catalogs that are being developed through machine learning. Their high dimensionality makes them challenging for seismologists to explore, and the conventional approaches noted above seem unlikely to be taking advantage of the wealth of new information available in the new generation of deeper catalogs. We suggest that, having first enabled the development of these catalogs, the statistical-learning techniques of data science are now poised to play an important role in uncovering new relationships within them. The obvious next step is to apply the techniques of machine learning in discovery mode 8 to discern new relationships encoded in the seismicity.

There are tantalizing indications that such an approach may lead to new insights. In double-direct-shear experiments, background signals that were thought to be uninformative random noise have instead been shown to encode information on the state of friction and the eventual time of failure of faults in a laboratory setting 9 . Well-controlled laboratory analogs to faults lack the geologic complexity of the Earth, yet, weak natural background vibrations of a similar sort, that again were thought to be random noise, have been shown to embody information that can be used to predict the onset time of slow slip events in the Cascadia subduction zone 10 . Finally, unsupervised deep learning, in which algorithms are used to discern patterns in data without the benefit of prior labels, applied to seismic waveform data uncovered precursory signals preceding the large and damaging 2017 landslide and tsunami in Greenland 11 .

These examples are compelling but come with the caveat that they are not representative of the typical fast rupture velocity earthquakes on tectonic faults that are of societal concern. For such earthquakes, however, there are also indications from state-of-the-art forecasting approaches that next-generation earthquake catalogs may contain information that will lead to progress. Physics-based forecasting models, which account for changes in the Coulomb failure stress due to antecedent earthquakes that favor the occurrence of subsequent earthquakes, have shown increasing skill such that they are competitive with, and are beginning to outperform, statistical models. Coulomb failure models benefit particularly from deeper catalogs because they include many more small magnitude earthquakes. These small earthquakes add predictive power through their secondary triggering effects tracking the evolution of the fine-scale stress field that ultimately controls earthquake nucleation in foreshock and aftershock sequences. They can also be used to define the emerging active structures that comprise fault networks and by doing so clarify the relevant components of stress that would act to trigger earthquakes 12 . Secondary triggering and background stress heterogeneity were shown to improve stress triggering models 13 but were most effective when they incorporated near‐real‐time aftershock data from the sequence as it unfolded 14 . We note that there is no reason why more complete earthquake catalogs, developed with pre-trained neural network models, cannot be created in real time as an earthquake sequence unfolds. Finally, despite the disappointing history of the search for precursors, due diligence requires that seismologists consider the pursuit of signals that might be precursory.

We conclude that it is now possible to image the activity on active fault systems with unprecedented spatial resolution. This will enable experimentation with familiar hypotheses and enable the formulation of new hypotheses. It seems certain that the underlying processes that drive earthquake occurrence are encoded in this next generation of earthquake catalogs, but we may not find them unless we put new effort into searching for them. Unsupervised learning methods 15 are particularly well-suited tool for that effort.

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Acknowledgements

This work is supported by the NERC-NSFGEO 13 funded project The Central Apennines Earthquake Sequence under a New Microscrope (NE/R0000794/1). G.C.B. was supported by Department of Energy (Basic Energy Sciences; Award DE-SC0020445). Thanks to Dr. Simone Mancini for preparing the figure.

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Beroza, G.C., Segou, M. & Mostafa Mousavi, S. Machine learning and earthquake forecasting—next steps. Nat Commun 12 , 4761 (2021). https://doi.org/10.1038/s41467-021-24952-6

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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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This document is the final project report of the Next Generation Attenuation for Central and Eastern North America (CENA) project (NGA-East). The NGA-East objective was to develop a new ground-motion characterization (GMC) model for the CENA region. The GMC model consists of a set of new ground-motion models (GMMs) for median and standard deviation of ground motions and their associated weights to be used with logic-trees in probabilistic seismic hazard analyses (PSHA). NGA-East is a large multidisciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER),...

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An Empirical Model for Fourier Amplitude Spectra using the NGA-West2 Database, PEER Report 2018-07

An empirical ground-motion model (GMM) for shallow crustal earthquakes in California and Nevada based on the NGA-West2 database [Ancheta et al. 2014] is presented. Rather than the traditional response spectrum GMM, this model is developed for the smoothed effective amplitude spectrum (EAS) as defined by PEER [Goulet et al. 2018]. The EAS is the orientation- independent horizontal component Fourier amplitude spectrum (FAS) of ground acceleration. The model is developed using a database dominated by California earthquakes, but takes advantage of crustal earthquake data worldwide to constrain...

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Earthquake prediction: a critical review

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Robert J. Geller, Earthquake prediction: a critical review, Geophysical Journal International , Volume 131, Issue 3, December 1997, Pages 425–450, https://doi.org/10.1111/j.1365-246X.1997.tb06588.x

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Earthquake prediction research has been conducted for over 100 years with no obvious successes. Claims of breakthroughs have failed to withstand scrutiny. Extensive searches have failed to find reliable precursors. Theoretical work suggests that faulting is a non-linear process which is highly sensitive to unmeasurably fine details of the state of the Earth in a large volume, not just in the immediate vicinity of the hypocentre. Any small earthquake thus has some probability of cascading into a large event. Reliable issuing of alarms of imminent large earthquakes appears to be effectively impossible.

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• News Releases

Researchers show that slow-moving earthquakes are controlled by rock permeability

University of Texas at Austin

Jackson School Associate Professor Nicola Tisato and Research Professor Harm Van Avendonk stop for a selfie in New Zealand in 2022.

Credit: Nicola Tisato / Jackson School of Geosciences

Earthquakes are the most dramatic and noteworthy results of tectonic plate movement. They are often destructive and deadly, or at the very least physically felt — they’re literally groundbreaking geological events. However not all tectonic movement results in effects that humans can perceive.

Slow slip events occur when pent up tectonic forces are released over the course of a few days or months, like an earthquake unfolding in slow motion. The more gradual movement means people won’t feel the earth shaking beneath their feet and buildings won’t collapse. But the lack of destruction does not make slow slip events less scientifically important. In fact, their role in the earthquake cycle may help lead to a better model to predict when earthquakes happen.

In a paper published recently in Geophysical Research Letters , a Jackson School of Geosciences research group led by Harm Van Avendonk, Nathan Bangs and Nicola Tisato explores how the makeup of rocks, specifically their permeability — or how easily fluids can flow through them — affects the frequency and intensity of slow slip events.

In 2019 and 2022, the group traveled to New Zealand’s North Island to collect rocks from several outcrops near the Hikurangi Margin. This is a subduction zone off New Zealand’s coast where slow slip events occur routinely, about once a year. The researchers brought back a cache of rocks to UT, where they tested their permeability and elastic properties.

Their tests showed how pores in the rocks could control the regular slow slip events at this subduction zone. Previous studies have suggested that a layer of impermeable rock at the top of the descending tectonic plate serves as a sealed lid, trapping fluid in the pores of underlying rock layers. As fluid accumulates beneath the seal, the pressure builds, eventually becoming high enough to trigger a slow slip event or earthquake. This event then breaks the impermeable seal, temporarily fracturing the rocks, allowing them to soak up fluids. Within a few months, the rocks heal and return to their initial permeability, and the cycle starts all over again.

In studying this cycle, Tisato and other researchers tested rocks from nearby surface outcrops which were once part of the earthquake fault deep underground. Previous permeability studies have been performed only on loose sediments that have been consolidated into solid rock.

“We are showing for the first time, using rocks that are representative of those at depth, that permeability is controlling (slow slip events),” he said.

Laura Wallace, a researcher at the University of Texas Institute for Geophysics and GEOMAR in Germany, has been studying slow slip events for more than 20 years, and was the first person to record slow slip events occurring in the Hikurangi Margin. She said that this paper adds more data points to inform the time scales over which the fault zone permeability changes can take place, possibly influencing the observed slow slip event cycles.

“It adds some additional data constraints on how this fault-valve process might work, how fluid cycling could work at the subduction zone — if that’s indeed what’s driving the cyclicity of these things,” Wallace said.

The ultimate goal of this research, Tisato said, is to understand why earthquakes happen and to eventually build a convincing model that can even predict them, a code scientists have yet to crack.

He and graduate student Jacob Allen are currently analyzing rock samples from the center of the margin and testing for differences in permeability. The rocks at the northern end of this subduction zone are richer in clays than those at the southern end. Because clays are malleable and can accommodate a lot of water and other fluids, they are ideally suited to trap, fracture and channel those fluids. That could explain why slow slip events on the northern end of the subduction zone happen frequently, whereas they occur rarely on the southern end, Tisato said.

“We have to go through the exercise of understanding why in the north of the Hikurangi Margin there are slow slips, and why in the south of the Hikurangi Margin we have fewer slow slips,” Tisato said. “Because if we understand that, then we have an additional step to go towards the prediction.”

Three graduate students from the Jackson School of Geosciences also contributed to this paper: Carolyn Bland, Kelly Olsen, and Andrew Gase.

Geophysical Research Letters

10.1029/2023GL103696

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Permeability and Elastic Properties of Rocks From the Northern Hikurangi Margin: Implications for Slow-Slip Events

Article Publication Date

23-Jan-2024

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Additional Multimedia

Do earthquake hazard maps predict higher shaking than actually occurred? Research finds discrepancy

A new study by Northwestern University researchers and coworkers explains a puzzling problem with maps of future earthquake shaking used to design earthquake-resistant buildings. The research was published May 1 in the journal Science Advances in a paper titled "Why do seismic hazard maps worldwide appear to overpredict historical intensity observations?"

Although seismologists have been making these maps for about 50 years, they know very little about how well they actually forecast shaking, because large damaging earthquakes are infrequent in any area.

To learn more, the Northwestern research team compiled shaking data from past earthquakes. These include CHIMP (California Historical Intensity Mapping Project) which combines data from seismometers with historical data (termed seismic intensity) that measures ground shaking caused by earthquakes from how it affected man-made structures and objects within the quake area. Intensity information can be gleaned from photographs of damage, first-hand or newspaper accounts, and oral history.

"We found a puzzling problem," said geophysicist Leah Salditch, the study's lead author and a recent Northwestern Ph.D. graduate. "Hazard maps for California as well as Japan, Italy, Nepal and France all seemed to overpredict the historically observed earthquake shaking intensities. The hazard maps were made by groups in different countries, but they all predicted higher shaking than observed."

In analyzing the possible causes, the research team discovered the issue was with the conversion equations used in comparing hazard maps predicting future earthquakes with actual shaking data, rather than systemic problems with the hazard modeling itself.

Salditch, who was in the research group of co-author Seth Stein, William Deering Professor Emeritus of Earth and Planetary Sciences at Northwestern, is now a geoscience peril advisor at Guy Carpenter & Company. Other Northwestern authors are Molly Gallahue and James Neely, also recent Ph.D. graduates from Stein's group.

Seismologists often say that "earthquakes don't kill people, buildings kill people"—most deaths in an earthquake are caused by collapsing buildings. As a result, society's best way to reduce deaths in future earthquakes is to construct buildings that should withstand them. However, because earthquake-resistant construction is expensive, communities need to balance its costs with other societal needs. For example, they can decide to put more steel in school buildings or hire more teachers.

To make these tough choices and design appropriately, policymakers and engineers use earthquake hazard maps that predict how much shaking to expect with certain probability over the many years buildings and other structures will be in use. These maps are based on assumptions about where and how often earthquakes in the area may happen, how big they will be and how much shaking they will cause.

In delving into the puzzle of why hazard maps from five different countries all predicted higher shaking than observed, the research team figured there had to be a problem with the maps, the data or both.

"We looked at a number of possible problems with the maps, including the extent that ground shaking depends on local geology, but none of these were big enough to explain the problem," Gallahue said.

If the problem wasn't in the maps, was it in the historical data?

"Probably not," said co-author Susan Hough from the U.S. Geological Survey. "The shaking data in the different countries were compiled using different techniques but were all lower than the maps predicted. If anything, historical intensities are expected to be inflated because historical sources tend to emphasize the most dramatic effects of shaking."

If there were no problems with the hazard maps and shaking data, why didn't they agree?

"There's a subtle problem," said co-author Norman Abrahamson of the University of California, Berkeley. "Hazard maps are quoted in physical units, whereas intensities are measured on a different scale, so one must be converted to the other. It turns out the conversion equations don't work that well for very strong shaking, so converting the map values overpredicts the intensity data.

"The problem isn't the maps but in the conversion," he said. "Changing the conversion solves most of the misfit between the maps and data. Moreover, a better description of the ground shaking should make things even stronger."

"This is an important and satisfying result," said co-author Neely, now at the University of Chicago. "Maps and data that seemed not to agree were both right. The problem was in comparing the two."

"We started this project 10 years ago and thought there might be serious problems with the hazard maps," Stein said. "Now it looks like there's no fundamental problem with them.

"Maps for some areas may not be that good for various reasons," he said.

"For example, in some places we don't know enough about the earthquake history or the shaking that large earthquakes produce because of the relatively short time span available. In others, the rate and size of earthquakes may be changing or just poorly understood. So, in some places, maps may overpredict future shaking and in others they may underpredict.

"Nature will sometimes surprise us. However, because the basic hazard mapping method looks sound, we can expect these maps to be fairly good and get better as we learn more."

More information: Leah Salditch, Why do seismic hazard maps worldwide appear to overpredict historical intensity observations?, Science Advances (2024). DOI: 10.1126/sciadv.adj9291 . www.science.org/doi/10.1126/sciadv.adj9291

Provided by Northwestern University

Utilizing digital technologies for rapid damage assessment and reconnaissance: the February 6, 2023 Kahramanmaraş-Türkiye earthquakes (Mw 7.7 and Mw 7.6)

• Original article
• Open access
• Published: 09 May 2024

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• Ahmet Bahaddin Ersoz 1 ,
• Onur Pekcan   ORCID: orcid.org/0000-0003-3603-5929 1 ,
• Murat Altun 2 ,
• Turker Teke 2 &
• Ozgur Aydogmus 2  

This paper presents a comprehensive overview of the rapid damage assessment and reconnaissance efforts following the devastating earthquakes on February 6, 2023, in Türkiye. It specifically focuses on implementing the SiteEye Disaster Plugin, an additional component of SiteEye software developed by i4 Company engineers and Middle East Technical University researchers. This tool played a critical role in managing and analyzing a massive dataset comprising over 28,000 images and videos. The research highlights the plugin’s innovative features, such as offline data collection, georeferenced-based layering, and an integrated damage classification system, significantly improving earthquake impact assessments’ accuracy and efficiency. It also underscores the importance of interdisciplinary collaboration involving national and international teams and the role of open data in disaster management. The findings demonstrate how digital technologies can transform the field of disaster response, offering new approaches for rapid assessment and effective management in the aftermath of seismic events. This research contributes valuable insights into enhancing disaster preparedness and response strategies, particularly in earthquake-prone areas.

Avoid common mistakes on your manuscript.

1 Introduction

On February 6, 2023, a sequence of devastating earthquakes struck Türkiye, leaving an unforgettable mark on history. These seismic events along the East Anatolian Fault (EAF), a zone of significant seismic activity, had far-reaching consequences for the southern and eastern parts of the nation. The initial quake’s moment magnitude (Mw) was 7.7, near Pazarcık, Kahramanmaraş, at 04:17 local Türkiye time (UTC + 3). Subsequently, a 7.6 earthquake occurred near Elbistan in Kahramanmaraş at 13:24 local Türkiye time (UTC + 3) (AFAD 2023 ). These seismic events occurred at depths of 8.6 km and 7.0 km, respectively, and were profoundly perceived over an extensive surface area. The first earthquake resulted in a fault approximately 190 km in length and 25 km in width. The subsequent earthquake occurred roughly 90 km to the northeast of the initial event. This second seismic event produced a rupture around 120 km long and 18 km wide (USGS 2023 ).

The earthquakes had geographical and social consequences in Türkiye, affecting more than 15 million people across eleven cities and an area of nearly 100,000 km 2 . Additionally, the earthquakes Mw 7.7 and Mw 7.6 shook portions of northern Syria. More than 35,000 structures collapsed, and nearly 180,000 were severely damaged due to the shaking, with most of the damage occurring in Hatay and Kahramanmaraş (TMMOB 2023 ). The casualty toll stood at around 50,000 fatalities and 107,000 injuries, according to the Ministry of Interior of Türkiye, highlighting the tragic human impact of the disaster (TRT Haber 2023 ). The widespread destruction caused by this event had a profound impact on numerous infrastructures—including industrial and residential structures, bridges, and transportation networks—resulting in economic losses surpassing $100 billion, with industrial facility damage accounting for a considerable portion of these losses (Avcil et al. 2023 ; Ozkula et al. 2023 ; Wang et al. 2023 ). Many reinforced concrete buildings in the earthquake region were severely damaged or collapsed entirely (Avcil 2023 ; Binici et al. 2023 ; Mertol et al. 2023 ; Vuran et al. 2024 ). In addition, cultural heritage sites, such as mosques and churches, also underwent damage and collapsed at multiple levels (Boyoğlu et al. 2023 ; Işık et al. 2023 ; Onat et al. 2023 ).

The earthquake’s devastation implied the criticality of efficient emergency response and disaster management procedures where digital information is vital. It was suggested that data collection and analysis efficiency in disaster reconnaissance might be improved using digital data modeling and communication strategies for information transfer (Schroeder et al. 2016 ). Earthquake databases, which contain a significant volume of images collected by engineering teams following hazard events, highlight a growing trend toward leveraging digital data sources for earthquake reconnaissance (Choi et al. 2022 ). Furthermore, increasing earthquake reconnaissance tasks utilize crowdsourcing and social media platforms as data sources, reflecting the trend toward combining digital data sources in earthquake reconnaissance (Contreras et al. 2021 ). The Preliminary Virtual Reconnaissance Report (PVRR) on the M7.2 Nippes Earthquake in Haiti (Kijewski-Correa et al. 2021a ) underscores the significance of digital platforms in enhancing the speed and accuracy of damage assessments. The “Did You Feel It?” (DYFI) system by Wald et al. ( 2011 ) exemplifies leveraging internet-based tools for rapid, widespread public engagement in seismic data collection. The integration of the MyShake smartphone application for crowdsourcing earthquake reports, as discussed by Kong et al. ( 2023 ), underscores the increasing dependence on digital technology for collecting seismic data.

In addition to underlining the utility of several earthquake missions, the significance of digital data collection utilizing omnidirectional images during post-earthquake reconnaissance missions has been underscored (Rossetto et al. 2014 ). Moreover, using unmanned aerial vehicles (UAVs) for aerial observation and rapid damage assessment in disaster management has been acknowledged as a crucial instrument for collecting digital data after seismic events (Stone et al. 2018 ). Likewise, reconnaissance organizations that collect wind, seismic, and coastal damage data during field reconnaissance missions upload and publish this information, emphasizing the significance of digital data sharing and dissemination in disaster risk management (Zwegliński 2020 ).

The comprehensive study on the catastrophic consequences of the Türkiye earthquake sequence highlights the need for digital innovation (Aktaş et al. 2023 ). The Structural Extreme Events Reconnaissance (StEER) Network’s community-centered approach (Kijewski-Correa et al. 2021b ) illustrates the role of digital tools in facilitating rapid knowledge sharing and data collection. The examination of traditional buildings’ seismic performance (Aktas et al. 2022a ) and the hybrid reconnaissance mission methodologies (Aktas et al. 2022b ) further reflect on the utility of digital and remote sensing methods in assessing earthquake impacts. The utilization of remote post-earthquake reconnaissance methodologies in response to the Haiti earthquake (Whitworth et al. 2022 ) and the evolving landscape of disaster reconnaissance missions (Aktas and So 2022 ) underscore the shift towards digital and hybrid approaches in disaster management.

The literature study illustrates the growing dependence on enhanced data modeling, remote sensing methods, and digital data for earthquake reconnaissance and disaster management (Giardina et al. 2023 ; Rathje and Franke 2016 ; Zwegliński 2020 ). Several tools have been developed that leverage digital data for post-disaster damage assessment and recovery. Roeslin et al. ( 2018 ) introduced a comprehensive tool for assessing building damage after earthquakes, effectively utilized in Mexico City following the 2017 Puebla earthquake. This tool, aiming for global applicability, includes detailed sections for recording various aspects of building damage and characteristics, underscoring the necessity for uniform methodologies in damage assessment. Similarly, Behrouzi and Pantoja ( 2018 ) focused on creating a software tool that analyzes high-resolution image data for structural damage post-earthquakes. Their tool simplifies the image tagging process, enhancing the training of deep learning algorithms for automatic damage identification. Additionally, Lin et al. ( 2019 ) developed the Real-time Individual Asset Attribute Collection Tool (RiACT), a framework for capturing attribute data of assets before and after disasters. RiACT facilitates efficient data capture, real-time transfer, and access to historical asset information, thereby supporting improved decision-making in disaster response. These tools represent significant advancements in integrating digital disaster management and recovery technology.

In light of these, the present study investigates the significance of data gathering and digital data management following the Kahramanmaraş-Türkiye earthquakes. It emphasizes the criticality of efficient data management practices for researchers to collect, analyze, and interpret enormous volumes of information to rapidly understand the scale of damage, identify weaknesses, and assess the performance of structures. It also implied the proper use of knowledge acquired from these analyses in guiding policymakers, revising design codes, and improving subsequent evaluations of earthquake risk.

During the 6th February Earthquakes, effective planning of large-scale, coordinated reconnaissance operations was possible by integrating diverse digital technologies, which also permitted instant access to field-acquired data (Cetin and Ilgac 2023 ). Researchers from several academic fields visited the affected locations, utilizing their respective areas of knowledge to analyze and record the effects on the infrastructure. Figure  1 displays images collected through the SiteEye Disaster Plugin from various locations, including reinforced concrete buildings, roads, and coastal structures, illustrating the diversity of both aerial and ground-level perspectives users provide. The collection of data was facilitated by national teams of professors from diverse universities, the Disaster and Emergency Management Presidency (AFAD), and the Ministry of Environment, Urbanization and Climate Change. Furthermore, the data-gathering procedure was joined by international teams such as EEFIT (Earthquake Engineering Field Investigation Team), GEER (Geotechnical Extreme Events Reconnaissance), and CAEES (The Canadian Association for Earthquake Engineering and Seismology). The excellence and scope of the gathered data are enhanced by this interdisciplinary collaboration, which takes place on a national and global scale; consequently, a more integrated knowledge of the effects of earthquakes is developed.

Data collected by researchers

The utilization of SiteEye ( www.siteeye.co ), a visual data management and cloud-based photogrammetry software, represents significant progress in digital data management. To respond to the earthquakes that struck Türkiye, engineers from i4 Company and researchers from the Department of Civil Engineering and Middle East Technical University worked together to develop the SiteEye Disaster Plugin voluntarily. This plugin is designed to facilitate earthquake examinations, enabling the integration of geolocated site data, including videos, drone footage, and ground images. The system handles the information effectively, merges the data with earthquake map layers, and facilitates damage interpretation. The data gathered consists of over 80 videos and more than 28,000 images, of which over 5,600 have labeled damage types immediately available to the users.

While SiteEye and tools like Fulcrum ( www.fulcrumapp.com ) and Survey123 ( www.survey123.arcgis.com ) aim to facilitate post-earthquake assessments, they differ significantly in customization and data integration capabilities. Fulcrum and Survey123 represent field data collection tools that allow users to customize and tailor forms to meet their specific requirements. In contrast, the SiteEye Disaster Plugin does not allow users to modify damage assessment forms; however, it has been methodically refined by incorporating expert feedback. Beyond the collection of damage classification data, as previously outlined, SiteEye enables researchers to upload either 2D/3D models, including orthophotos/point clouds, or generate such models through photogrammetry employing UAV photographs, thereby facilitating comprehensive terrain analysis. Furthermore, SiteEye integrates various seismological data sets, such as fault lines, surface ruptures, moment magnitude (Mw), peak ground acceleration (PGA), and peak ground velocity (PGV) distributions, into interactive maps as supplementary information layers, thereby presenting a multifaceted perspective on the impact of earthquakes.

Reflecting on the utility of the SiteEye Disaster Plugin, it is evident that its development was timely and critical for enhancing the effectiveness of earthquake reconnaissance efforts. This tool was designed and developed shortly after the two catastrophic earthquakes. It comprises a collection of purposefully engineered functionalities to optimize and enhance damage assessment investigations. The researchers’ improved efficiency and accuracy in data collection and evaluation, made possible by the SiteEye Disaster Plugin, exemplifies the platform’s critical contribution to disaster management. The procedures for data collection, management, and analysis utilizing the SiteEye Disaster Plugin are elaborated upon in the following sections of this paper. This demonstrates how digital technologies transform field studies conducted after earthquakes and provide novel approaches to rapid damage assessment and reconnaissance.

2 Development of the plugin

SiteEye, a cloud-based data management system, was created by developers and researchers affiliated with Middle East Technical University. Featuring a multi-level architecture, it efficiently facilitates data storage in the cloud and operates on several platforms, including web, iOS, and Android. SiteEye is utilized for construction site documentation, enabling extensive data collection through web and mobile applications. The mobile application efficiently manages ground data, gathering media directly from construction job sites to support detailed monitoring and reporting. Figure  2 illustrates the user interfaces of the SiteEye mobile application, exemplifying its application in collecting earthquake imagery data. SiteEye is distinguished by its georeferenced-based layering technology. This approach facilitates the utilization of diverse geographical layers, which serve as a data collection and processing framework. The software’s strength lies in its capability to autonomously extract geographic coordinates from the EXIF and embedded metadata of files, therefore cartographically representing this information with pinpoint accuracy.

Mobile platform interfaces of SiteEye Disaster lugin

In the aftermath of two earthquakes, authors have explored strategies to enhance support for researchers conducting field investigations. Within the initial week, researchers initiated the collection of ground images from various affected cities, with damage classification labels determined through consultation with earthquake experts. After the beginning of SiteEye Disaster Plugin development, deficiencies in SiteEye’s web and mobile platform were identified by both the development team and researchers. One of the first challenges was rendering media annotations within the map and list as the dataset expanded. Performance optimizations were instituted to mitigate this, including adopting virtual scrolling for the list and clustering annotation markers on the map based on zoom level. Additionally, absent functionalities on the mobile application, such as map-view and saving media for later upload under optimal service conditions, were addressed.

Managing substantial quantities of data is an essential component of SiteEye’s operation. SiteEye platform has been used in the field surveying industry for the last seven years, handling the visualization of gigabytes of mesh and point cloud data generated using UAV (Unmanned Aerial Vehicle) imagery on a web browser, which makes it suitable for handling the substantial quantities of image data an earthquake aftermath provides. In the month following the February 6, 2023 earthquakes, 28,000 images and videos were uploaded to the servers, and it was critical to preserve optimal system performance. In order to handle this, the system implements clustering methods for proper visualization. By utilizing marker clustering, data locations are displayed on-screen at many zoom levels (Fig.  3 ). In this figure, markers are colored red if the corresponding cluster contains more than 15 images, blue if it contains fewer, and they are green if it is a singular image. This attribute is particularly critical as it facilitates the organization of enormous quantities of data into feasible segments, categorized based on the contributions of the researchers. Enriching the dataset with openly available geotagged data and facilitating data uploads from affected citizens was initially considered for data collection. However, given the plugin’s principal aim of aiding earthquake researchers in their investigations, it was decided to restrict data collection and labeling exclusively to these experts. Despite data collection and processing being undertaken by these experts, the decision to share this data for broader research endeavors was made and implemented by collecting access requests from individual researchers and groups.

Marker clustering on the web interface

Due to the extensive disruptions in communication and internet access during the earthquakes, SiteEye modified its data collection methodologies to accommodate the challenging site conditions. Researchers can collect data in various ways using the mobile application: when internet access is available, they can shoot photographs or videos with direct geolocation tagging and upload them to SiteEye; otherwise, they can gather data offline for subsequent upload. When internet connectivity is restored, geolocation information data can be loaded into the application from the mobile device’s gallery.

SiteEye Disaster Plugin has been thoughtfully constructed to assist researchers in accurately and effortlessly classifying and evaluating data. An essential component of this framework is integrating a damage classification system. The interface incorporates this system, which enables field researchers to efficiently categorize their data according to the observed degree of damage and the type of structure. Determining the level of earthquake damage and strategizing for subsequent recovery and reconstruction endeavors require this classification. An exhaustive effort was devoted to defining classes throughout the construction of this classification system, which included consultation with earthquake experts from various fields. The classes of damages are given in Table  1 in the Appendices. The building damage categories are based on the official classification of earthquake damages on the structures reported by the Ministry of Environment, Urbanization and Climate Change, Republic of Türkiye ( 2024 ), given in Table  2 in Appendices. Immediately classified damage data benefited the engineers and researchers working in the field for reconnaissance studies as the damage was distributed in 11 provinces. This way SiteEye Disaster Plugin contributed to proper guidance for the reconnaissance. An illustrative example of the SiteEye Disaster Plugin’s impact on ground operations involves the route selection of international teams. Before their field visit, they checked the SiteEye platform to select areas they intended to focus on more closely. Specifically, the GEER team actively utilized the SiteEye platform for their visiting area selection, significantly optimizing their reconnaissance efficiency. Moreover, they also contributed their data to the platform, enriching the database with firsthand, valuable insights from the field. This synergy between SiteEye and its users facilitated targeted reconnaissance efforts and fostered a collaborative environment for data sharing and analysis.

Furthermore, the web interface, illustrated in Fig.  4 , has undergone optimization to simplify and enhance the user experience while selecting damage types, contributing to overall data analysis and reporting efficiency. Specifically, SiteEye introduces a feature allowing users to create title and description records for each photo beyond damage classifications. This capability enables users to input detailed text descriptions and pertinent tags for every image, making the dataset more informative and searchable. For instance, a user might tag a photo with “collapsed bridge or main street,” providing specific context that can be filtered and queried. This attribute is particularly advantageous for researchers, allowing them to explain their field data comprehensively. Such detailed records prove beneficial, especially when the collected data is queried by institutions and researchers from countries outside Türkiye, allowing for precise filtering based on text descriptions.

Damage classification steps

A disclosure method was implemented to attribute researchers’ contributions regarding intellectual property and data ownership. The researcher’s name obtaining the data is watermarked on every image published to the portal. In a collaborative context, this function is critical for preserving the integrity of the data and acknowledging the efforts of individual researchers.

One notable improvement to SiteEye technology is the use of Google Street View imagery. Google Street View has become a highly beneficial instrument in earthquake damage assessment, performing virtual inspections of constructed surroundings. Evangelista et al. ( 2022 ) emphasized Google Street View’s capability to capture vital information required to assess the effects of disasters on urban infrastructure. Thus, the inclusion of this functionality emerged from the requirement of researchers to observe the state of the built environment and its components before the earthquake to facilitate comparative analyses (Fig.  5 ). Employing this integration, researchers are granted access to Google Street View images that precisely align with the geographical coordinates of the data they provide.

Street view comparison

3 Data collection and processing

Data acquisition for the post-earthquake assessment was initiated immediately after the earthquakes. Rapid mobilization of national and international research teams to the earthquake-affected regions followed. Data was collected with the assistance of these teams, including images and videos obtained from the ground and aerial imagery utilizing UAVs. The timely accessibility of information was pivotal in enabling the systematic organization of reconnaissance operations. A critical component of this data collection effort was the consistent continuation of communication with field researchers. This communication was crucial in informing researchers on adequately utilizing the SiteEye tool. In addition, it facilitated the prompt reporting of any discovered problems in real-time, empowering the SiteEye Disaster Plugin development team to resolve concerns immediately and guarantee the application’s seamless operation within demanding field conditions.

The primary method for collecting field data involved using the SiteEye mobile application and its corresponding web plugin. The framework of data flow is given in Fig.  6 . Following the data collection, the data processing phase contained a sequence of procedures to manage the gathered images and videos efficiently. These procedures included sorting and categorization based on geographic and structural attributes, tagging with predefined classifications, geocoding to enhance searchability and analysis, the integration of seismological data for multidimensional analysis, and utilizing the photogrammetry engine capabilities to generate detailed 3D models from aerial imagery for advanced spatial analysis. However, before delving into these processes, it was crucial to visualize the data on a map first. This initial step of visual representation highlighted the geographical distribution of the earthquake’s consequences across the impacted regions, serving as a foundational activity for subsequent analysis and decision-making processes (Fig.  7 ).

Framework of rapid earthquake reconnaissance via SiteEye Disaster Plugin

Data distribution on the map

In order to further enhance the practical interpretation of the information, the data was carefully labeled with the predetermined classifications using the structure type, substructure type, and types of damage among these particulars. Experts from numerous disciplines, including coastal, geotechnical, and structural engineering, compiled an exhaustive list from which the tags were chosen. A total of 5658 images were classified. 4387 geotechnical photos were incorporated into this comprehensive classification, which yielded vital insights regarding the earthquake’s geotechnical/geological consequences. Furthermore, 1160 images were allocated for evaluating the buildings, and 111 images were explicitly targeted at coastal structures to underscore the earthquake’s impact on these vital assets. The majority of data in SiteEye is primarily geotechnical because the platform’s user base consists mainly of geotechnical researchers, leading to a higher volume of contributions in this area. The graphical distribution of the specifics of these classes can be seen in Figs.  8 , 9 , and 10 . The values presented alongside the feature titles represent the number of photos in the figures.

Damage classification of geotechnical structures

Damage classification of buildings

Damage classification of coastal structures

An integral component of the data processing procedure entailed geocoding the image coordinates. Geocoding involves converting geographic coordinates into more comprehensible and searchable formats, such as city, district, and street names. The incorporation of geographical information into the images significantly facilitated the process of filtering and classifying the data. An example of tagging an image would be “Yarbaşı, Pazarcık, Kahramanmaraş,” which would enable researchers to filter images according to specific locations or cities. The organization of this data played a critical role in analyzing the spatial distribution of damage and developing targeted response strategies.

The data processing phase integrated various seismological data into the mapping and imagery systems. The components mentioned above comprised fault lines, surface ruptures, moment magnitude (Mw), peak ground acceleration (PGA), and peak ground velocity (PGV) distributions. By utilizing the layering technique, it was possible to represent various data types simultaneously, thereby providing a multidimensional view of the earthquake’s effects. Those map layers are given in Fig.  11 . SiteEye Disaster Plugin, in this sense, uniquely delivered this information online as it interacted with the field researcher’s findings and provided them to others.

In addition to its collection and visualization of standard imagery and videos, the SiteEye software leverages photogrammetry techniques to transform aerial imagery into three-dimensional models. This process predominantly utilizes drone images, ideal for capturing high-resolution data of the earth’s surface from various angles. Through a sophisticated Structure from Motion (SfM) (Schonberger and Frahm 2016 ) and Multi-View Stereo (MVS) (Furukawa and Ponce 2010 ) pipeline, SiteEye constructs detailed 3D point clouds and orthophotos. This SfM-MVS methodology enables the accurate modeling of surface geometries, thereby facilitating a comprehensive analysis of physical terrain attributes. The generated 3D point clouds are particularly invaluable in assessing earthquake-damaged regions, allowing for an in-depth inspection of buildings with structural damage. This capability aids in the immediate evaluation of the damage and the strategic planning of reconstruction efforts. Utilizing the photogrammetry capabilities of the SiteEye software, several earthquake-affected regions were successfully transformed into detailed 3D models.

Meanwhile, the orthophotos produced by SiteEye provide an essential bird’s-eye view of the affected areas, making it possible to monitor the availability of tent areas—where tents were installed for earthquake victims—. Figure  12 exemplifies this by showcasing the tent areas to shelter earthquake victims. Also, the online accessibility of the data guarantees that professionals from various geographical locations and disciplines can readily access, collaborate on, and share it. This feature facilitates seamless teamwork across different geographies, allowing users to engage in real-time discussions, make precise model adjustments, and share insights without needing physical presence. Moreover, the cloud storage capability ensures that all modifications and annotations are saved and instantly accessible to all team members, streamlining the collaborative process and enhancing the efficiency of analyzing and planning reconstruction efforts for earthquake-affected areas.

3D data generated by SiteEye

Finally, an essential field research component used markers in the mapping system to designate significant locations, such as soil sample positions. The markers functioned as critical reference points on the map, facilitating researchers and analysts to identify and examine particular sites of interest rapidly. By facilitating the correlation between physical samples and observations and the corresponding geospatial data, this function significantly enhanced the analysis and comprehension of the earthquake’s impacts as a whole.

4 Discussion

SiteEye Disaster Plugin demonstrated its ability to adapt and respond effectively to the complex data challenges from the February 6, 2023, earthquakes in Türkiye. This plugin efficiently organizes and visualizes massive datasets in order to accommodate the collection of more than 28,000 images and videos. Immediate processing and data analysis are critical for effective response and recovery action plans, making this capability crucial in disaster scenarios. This contribution emphasizes the significance of scalable and adaptable data management systems in crises, which can significantly affect the speed and efficacy of disaster relief operations.

SiteEye’s adaptable approaches to data collection effectively tackle significant obstacles in disaster management, specifically in the aftermath of such events. The software’s design incorporates protections for data collection in disaster zones, taking into account the potential disruptions in communications that may occur even in the absence of internet connectivity. This function guarantees uninterrupted data gathering, essential for the timely and detailed damage evaluation. By permitting data collection offline and subsequently uploading it once connectivity is restored, SiteEye guarantees the preservation of critical information and improves the efficiency of field operations under challenging circumstances.

The implementation of georeferenced-based layering technology in SiteEye represents a significant advancement in the visualization and management of geographical data. The capability of this technology to independently extract geographic coordinates from file metadata substantially enhances the accuracy and dependability of spatial data analysis. It facilitates the integration of diverse geographical layers, offering a more comprehensive data presentation and interpretation framework. This technological progress improves the precision of data visualization. It facilitates more informed and up-to-date decision-making in disaster response and urban planning, particularly in the aftermath of earthquakes.

SiteEye contributed substantially to disaster assessment by using photogrammetry technology to generate three-dimensional models from aerial imagery. This technology provides a dense point cloud representation of the impacted terrain, enabling in-depth topographical evaluations and spatial analyses. In understanding the extent and particulars of the damage, the capacity to produce sophisticated 3D models is essential; this capability improves the precision of assessments and facilitates the organization of reconstruction endeavors.

Developing an integrated damage classification system into SiteEye significantly advances earthquake research and disaster management. By allowing field researchers to classify data according to observed damage, this system facilitates the evaluation of earthquake impacts in a streamlined manner. The participation of experts in earthquake engineering in the process of establishing classification criteria guarantees that the system is comprehensive and pertinent. This characteristic facilitates immediate disaster response and supports critical long-term recovery planning and infrastructure resilience evaluation, substantially contributing to structural engineering and urban planning in earthquake-prone areas. This technology plays an essential role in urban planning by enabling planners to identify and map areas at high risk for earthquakes more accurately, ensuring that zoning and development regulations can be appropriately adjusted to mitigate risk. Additionally, the improved visualization technologies integral to this system aid in designing and optimizing evacuation routes, enhancing the preparedness and efficiency of emergency responses by ensuring immediate access to emergency services following a disaster.

The tool also facilitates collaborative efforts by streamlining data collection and interpretation integration between remote and on-site researchers. It aids in efficiently planning paths and strategies by utilizing previously uploaded images and analyzing the distribution of damage within specific locations. Researchers were able to plan their site visits efficiently with the assistance of classified information that was shared regarding the earthquake zone. By maximizing the coverage and efficiency of data collection efforts and guaranteeing exhaustive documentation of the extensive damage, this strategic planning was crucial. Gaining insight into the earthquake’s far-reaching effects required implementing such a methodical strategy. Additionally, its analysis was significantly impacted by the structured nature of the gathered data.

A noteworthy data analysis element was the application of Google Street View imagery captured before the earthquake. Employing SiteEye and Google Street View comparison, the destruction caused by the earthquake was clearly illustrated, offering a distinct before-and-after viewpoint. This type of imagery was crucial in understanding the extent of damage sustained by numerous structures.

The acquisition of diverse data from locations affected by widespread earthquakes is critical. It facilitates the derivation of significant patterns corresponding to the destructive nature of the occurrences. Examining and interpreting these patterns are of the highest concern for post-earthquake research, as they enhance response strategies and offer valuable insights for subsequent disaster management. For example, adapting the plugin for Istanbul, an earthquake-prone area in Türkiye, is essential to enhance earthquake preparedness. The tool’s advanced georeferenced visualizations enable emergency teams to efficiently plan and prioritize responses based on anticipated damage locations. Collaborative efforts with local stakeholders, including government agencies and educational institutions, would ensure the tool’s integration into disaster preparedness and urban planning processes. This integration enhances immediate response capabilities and fosters a community resilience and preparedness culture.

With its diverse capabilities, the SiteEye Disaster Plugin is an indispensable component of these visual inspection and analysis procedures. Some of the gathered data was made available to the public to promote collaboration and advance earthquake research. The shared dataset is an asset for researchers across the globe, facilitating a more comprehensive examination and comprehension of the consequences of the earthquake. In brief, the collective endeavors encompassing data collection, management, and analysis have substantially contributed to earthquake investigation of earthquake impacts. In addition to strengthening our comprehension of earthquake occurrences, these discoveries establish a fundamental basis for greater wisdom and efficacy in our approaches toward forthcoming earthquakes.

5 Conclusions and future works

February 6, 2023, earthquakes in Türkiye, magnitudes of 7.7 and 7.6, have underscored the critical importance of rapid and efficient disaster response and management, especially in earthquake-prone regions. The study presented here provides a comprehensive examination of the utilization of digital technologies in earthquake reconnaissance and disaster management. Integrating the SiteEye Disaster Plugin has proven to be a pivotal advancement in disaster assessment. The plugin has shown remarkable capabilities in organizing, visualizing, and managing large-scale datasets. Its features, such as offline data collection, georeferenced-based layering technology, and an integrated damage classification system, have significantly enhanced the efficiency and accuracy of earthquake impact assessments. The use of photogrammetry technology for creating detailed 3D models and incorporating Google Street View for comparative analyses further exemplifies the plugin’s contribution to understanding and managing the aftermath of seismic events. Furthermore, these data can also be utilized as educational material for training future researchers and students in earthquake engineering.

The study highlights the importance of collaborative and interdisciplinary efforts in disaster management. The involvement of national and international teams was significant in the extensive data collection and analysis process. Sharing a portion of this data as an open dataset is a testament to the commitment to collaborative research in understanding and mitigating the impacts of earthquakes. It is important to note that only a portion of the data is shared publicly due to the preferences of individual contributors, who retain the choice of whether to make their data available for open access.

In conclusion, the response to the February 2023 earthquakes in Türkiye, facilitated by the SiteEye Disaster Plugin, represents a significant leap forward in digital disaster management. The insights gained from this experience are invaluable for enhancing preparedness and response strategies for future seismic events, thereby contributing to the resilience and safety of vulnerable communities worldwide. As a future work, the integration of machine learning could help analyze and classify earthquake data, building upon the SiteEye Disaster Plugin’s capabilities. Specifically, machine learning algorithms can be trained to automatically classify images of earthquake damage, such as structural and geotechnical damage. Beyond the classification, these algorithms can also determine the severity of damage. Moreover, the algorithms can effectively compare pre- and post-earthquake images to detect damage, which is crucial for quickly identifying heavily affected areas and streamlining damage assessment. Considering these capabilities, AI-driven techniques will be developed to enhance damage assessment, perform intricate geospatial analyses, and refine photogrammetry models for more accurate 3D representations of affected areas.

Data availability

A considerable effort was initiated to reveal some of the gathered data to the public to enhance the breadth of earthquake research and promote cooperative analysis. Following this endeavor, consent was obtained from the data owners. The dataset, comprising relevant geospatial information, videos, and images, has been available to scientists and practitioners interested in earthquake investigations. In order to obtain access to this publicly available dataset, users should complete the registration process on the SiteEye platform via www.siteeye.co freely.

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Acknowledgements

We extend our sincere thanks to Furkan Aydoğan, Ediz Karaali, Yunus Eren Kaya, Görkem Kılıç, Muzaffer Ata Özbilen, and Serhat Erinmez for their exceptional support. Their expertise and commitment were crucial in advancing our project, and we are deeply grateful for their contributions. Their efforts have significantly enhanced the quality and success of our work.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The aforementioned plugin was developed voluntarily.

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Ahmet Bahaddin Ersoz & Onur Pekcan

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Ersoz, A.B., Pekcan, O., Altun, M. et al. Utilizing digital technologies for rapid damage assessment and reconnaissance: the February 6, 2023 Kahramanmaraş-Türkiye earthquakes (Mw 7.7 and Mw 7.6). Bull Earthquake Eng (2024). https://doi.org/10.1007/s10518-024-01925-w

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Published : 09 May 2024

DOI : https://doi.org/10.1007/s10518-024-01925-w

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UMaine News

NSF backs UMaine research to lessen earthquake damage with new, inexpensive method

On the backside of a piece of paper he found at his desk, University of Maine associate professor of geotechnical engineering Aaron Gallant drew a microscopic view of soil to explain the foundation of his research. Inside a small square, he penned in circles of different sizes for two types of particles and wavy lines for groundwater flow through the soil. The smaller of the two particles represented tiny gas bubbles floating in water between soil grains, the larger of the two. 

Although the soil is loose and deformable, Gallant said it’s stable as long as soil particles can compact and displace the groundwater. It can lose stability when vibrations from an earthquake create pressure in the ground and liquify soil with high water content. Known as liquefaction, the process is capable of destabilizing foundations under bridges, homes, oil tanks and other facilities, causing them to collapse.

The tiny gas bubbles represented in Gallant’s illustration may have the ability to effectively mitigate the damaging impacts of liquefaction by suppressing groundwater pressurization. 

In partnership with Portland State University (PSU) in Oregon, Gallant and two other researchers from UMaine — assistant professor of civil and environmental engineering Luis Zambrano-Cruzatty and graduate student Andres Cordoba — are testing a new method of fortifying water-saturated soil. The team will be stimulating microbes in the ground that generate nitrogen gas to buffer ground pressure created by earthquakes, like shocks on a car. 

The National Science Foundation (NSF) selected the team in a competitive grant process for an award of $961,871 to conduct the research, with UMaine’s share totaling $365,594.

“We’re thinking about how the ground interacts with a built environment,” said Gallant. “One of the things that is very difficult to mitigate is this idea of liquefaction, when the soil turns from a solid state essentially into what’s considered a liquid state.”

Regions prone to earthquakes and near water, such as the west coast, greater Charleston area in South Carolina and New Madrid seismic zone in the midwest, are specifically in danger of soil liquefaction in the U.S.

The inspiration

“This is something that can be very destructive,” said Gallant. “The specific site that we’re concentrating on here is the critical energy infrastructure hub in Portland, Oregon. They have a lot of oil tanks sitting on liquefiable soils. If a big earthquake happens, all of that oil and the state’s energy supplies are susceptible to this big, potentially catastrophic event.”

If the soil liquified during an earthquake, Oregon could lose 90% of its petroleum reserves . 

Diane Moug, assistant professor of civil and environmental engineering at PSU, said since Oregon doesn’t have fuel refineries, all the liquid fuel is shipped to the infrastructure hub then distributed. The fuel tanks were built before the state understood the seismic hazard.

“These fuel tanks are not seismically robust,” said Moug. “They’re on soil that will fail in an earthquake.”

Besides the loss of liquid fuel for the entire state, Moug said people are concerned that if the tanks fail they will spill into the Willamette River causing harm to fisheries and ecosystems. 

Since tanks and infrastructure are already there, she said improving the soil underneath is difficult. The gas-generating method the research team is studying is one of a few that can treat ground where infrastructure exists. 

“There’s this nice synergy of us looking at how effective this method is for reducing earthquake hazards, and Gallant and his team are looking at how long it lasts,” said Moug. “We see those two questions as really intertwined. You can’t have one without the other.”

The multi-point research is an attempt to not only create a solution for soil liquefaction, but to make it cost effective and widely accessible — for resource-rich countries like the U.S. and other places like rural Indonesia that often experience earthquakes and are prone to liquefaction. 

Following a 2018 earthquake in Indonesia, Gallant joined an NSF-funded research team associated with the Geotechnical Extreme Events Reconnaissance Association that traveled to the Southeast Asian country and studied the Palu-Donggala quake. Researchers concluded liquefaction from the earthquake had triggered catastrophic landslides that caused mass destruction and the death of more than 4,000 people. 

New technique

Gallant’s research into mitigating liquefaction dates back to 2016 when he joined UMaine.

This most recent segment funded by the near $1 million NSF grant is specifically focused on fortifying silt soils over a long period of time by generating nitrogen gas with microbially induced desaturation (MID). Inexpensive compared to reinforcing building foundations or driving concrete pillars deep into the ground, the generated nitrogen gas absorbs pressure from earthquakes by filling empty space between soil particles and groundwater. 

“It’d be very challenging to take a straw, blow bubbles into the soil and get it uniformly mixed,” said Gallant. “With MID, we’re essentially stimulating the microbes and getting a denitrification process rolling that will allow us to generate nitrogen gas in place.”

In addition to being a cost effective solution, Gallant said nitrogen gas isn’t damaging to the environment and will last a long time in the soil, potentially decades to a century. It doesn’t dissolve easily in water, and the atmospheric pressure of nitrogen — the gas with the highest naturally occurring concentration in the air — significantly contributes to the persistence and longevity of gas entrapped in the soil column.

“What happens when you open a Coke bottle? The CO 2 comes out,” said Gallant. “But why was it staying there before? Because it had a lot of CO 2 pressure inside the bottle.” The same concept applies to nitrogen gas in groundwater.

Two challenges will be knowing whether the gas is staying in place and predicting how long it will be there. If the research team can successfully address these concerns, MID and other desaturation techniques have the ability to transform how civil engineers protect infrastructure susceptible to liquefaction.

Contact: Ashley Yates; [email protected]

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• [News] Controlled Market Demand Amid Earthquake Impact, Second Quarter DRAM Prices Expected to Rise by Over 20%

According to a report from TechNews , there has been a significant surge in demand in the memory market, driving prices steadily upward. This surge has led to the latest quotations from Korean memory manufacturers, with DDR5 prices set to increase by 13% in May.

Additionally, DDR4 prices are also expected to rise by 10%. As for DDR3, which currently serves as the main supply for Taiwanese memory manufacturers, there is also room for a 10% to 15% increase. Overall, contract prices for the second quarter are anticipated to rise by 20% to 25%.

Following in the wake of an earthquake that struck on April 3rd, TrendForce undertook an in-depth analysis of its effects on the DRAM industry , uncovering a sector that has shown remarkable resilience and faced minimal interruptions. Despite some damage and the necessity for inspections or disposal of wafers among suppliers, the facilities’ strong earthquake preparedness of the facilities has kept the overall impact to a minimum.

Leading DRAM producers, including Micron, Nanya, PSMC, and Winbond had all returned to full operational status by April 8th. In particular, Micron’s progression to cutting-edge processes—specifically the 1alpha and 1beta nm technologies—is anticipated to significantly alter the landscape of DRAM bit production. In contrast, other Taiwanese DRAM manufacturers are still working with 38 and 25nm processes, contributing less to total output. TrendForce estimates that the earthquake’s effect on DRAM production for the second quarter will be limited to a manageable 1%.

With the earthquake’s impact under control, for each manufacturer, Micron temporarily suspended quotations due to the earthquake’s impact on production. After completing the assessment of losses, it notified customers of a 25% increase in DRAM and SSD contract prices.

Additionally, due to Samsung’s production line conversion, the early cessation of DDR3 production has prompted many customers to turn to Nanya and Winbond for procurement orders, leading to the completion of product verification. As a result, Nanya and Winbond have informed customers in the second quarter that DDR3 prices are expected to increase by 10-15%.

Per TrendForce’s observations, as the three major memory manufacturers continue to transition their production capacity to highly demanded products such as HBM and DDR5, the production capacity of products like DDR4 and DDR3 has significantly decreased.

Overall, the supply-demand gap is expected to exceed 20-30% by the second half of 2024. This situation will lead to a substantial increase in DDR3 prices in the second half of the year, with price hikes reaching 50-100% as current prices remain below costs. This scenario will also be advantageous for the operational performance of various domestic memory manufacturers.

• DRAM Manufacturers Gradually Resume Production, Impact on Total Q2 DRAM Output Estimated to Be Less Than 1%, Says TrendForce
• [Insights] DRAM Spot Prices Expected to Decline Post-Manufacturer Quoting Resumption

Please note that this article cites information from  TechNews .

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