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Legacy surface change analyses of the 1993 Rock Valley earthquake sequence for direct comparison to planned NA-22 underground conventional high-explosive experiments (Source Physics Experiment 3 (SPE3) - RV/DC Task 1.3 FY22 Final Report)

Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.

42 ENGINEERING↗

Dynamic Rupture Simulations of the Mw7.2 1992 Landers,California, Earthquake

An important part of the simulation technique implemented in the Recipe is the earthquake rupture characterization based on established empirical scaling relationships between source parameters and seismic moment. The scaling relationships used in the rupture characterization are derived from recorded data and are tested against dynamic and kinematic rupture models of crustal earthquakes. Currently, a revised three-stage source scaling relationship between the source rupture area and seismic moment of inland crustal earthquakes, initially proposed by Irikura and Miyake, is being used. Recent analysis of results obtained with the Recipe for the 2016 M7.1 Kumamoto earthquake performed by several researchers, including the PI, Arben Pitarka, indicate that in order to reproduce the near-fault long-period ground motions and the permanent displacements, observed during surface-rupturing earthquakes, the recipe needs modifications to the kinematic rupture parametrization in the shallow crustal weak zone. In particular, improvements are required in characterizing the slip rate function at shallow depths, above the sesimogenic zone. The main objective of this research project was the development of a dynamic rupture model for the Mw7.2 1992 Landers, California earthquake. The dynamic rupture model was used to derive kinematic rupture parameters, such as slip, slip-rate, rupture time distributions, and slip rate functions in the weak (0-4km) and seismogenic crustal zones. In addition to the knowledge gained from the dynamic rupture modeling of Chi-chi, Ridgecrest, Kumamoto and other crustal earthquakes, studied by the PI, the rupture characterization of the Landers earthquake can be helpful in updating the conventional recipe for broadband ground motion simulations of crustal earthquakes with shallow rupture. The project emphasizes the need for performing physics-based earthquake rupture modeling which ultimately will inform further improvements of rupture characterization. In particular, the modeling of rupture dynamics for recorded earthquakes, and numerical simulations for different rupture scenarios, provides physical constrains on slip evolution in time and space, used in adjustments of empirical source scaling relationships for large crustal earthquakes.

58 GEOSCIENCES↗

Validating of Atmospheric Signals Associated with some of the Major Earthquakes in Asia (2003-2009)

The recent catastrophic earthquake in Haiti (January 2010) has provided and renewed interest in the important question of the existence of precursory signals related to strong earthquakes. Latest studies (VESTO workshop in Japan 2009) have shown that there were precursory atmospheric signals observed on the ground and in space associated with several recent earthquakes. The major question, still widely debated in the scientific community is whether such signals systematically precede major earthquakes. To address this problem we have started to validate the anomalous atmospheric signals during the occurrence of large earthquakes. Our approach is based on integration analysis of several physical and environmental parameters (thermal infrared radiation, electron concentration in the ionosphere, Radon/ion activities, air temperature and seismicity) that were found to be associated with earthquakes. We performed hind-cast detection over three different regions with high seismicity Taiwan, Japan and Kamchatka for the period of 2003-2009. We are using existing thermal satellite data (Aqua and POES); in situ atmospheric data (NOAA/NCEP); and ionospheric variability data (GPS/TEC and DEMETER). The first part of this validation included 42 major earthquakes (M greater than 5.9): 10 events in Taiwan, 15 events in Japan, 15 events in Kamchatka and four most recent events for M8.0 Wenchuan earthquake (May 2008) in China and M7.9 Samoa earthquakes (Sep 2009). Our initial results suggest a systematic appearance of atmospheric anomalies near the epicentral area, 1 to 5 days prior to the largest earthquakes, that could be explained by a coupling process between the observed physical parameters, and the earthquake preparation processes.

Ouzounov, D. P.↗

Multi-Parameter Observation and Detection of Pre-Earthquake Signals in Seismically Active Areas

The recent large earthquakes (M9.0 Tohoku, 03/2011; M7.0 Haiti, 01/2010; M6.7 L Aquila, 04/2008; and M7.9 Wenchuan 05/2008) have renewed interest in pre-anomalous seismic signals associated with them. Recent workshops (DEMETER 2006, 2011 and VESTO 2009 ) have shown that there were precursory atmospheric /ionospheric signals observed in space prior to these events. Our initial results indicate that no single pre-earthquake observation (seismic, magnetic field, electric field, thermal infrared [TIR], or GPS/TEC) can provide a consistent and successful global scale early warning. This is most likely due to complexity and chaotic nature of earthquakes and the limitation in existing ground (temporal/spatial) and global satellite observations. In this study we analyze preseismic temporal and spatial variations (gas/radon counting rate, atmospheric temperature and humidity change, long-wave radiation transitions and ionospheric electron density/plasma variations) which we propose occur before the onset of major earthquakes:. We propose an Integrated Space -- Terrestrial Framework (ISTF), as a different approach for revealing pre-earthquake phenomena in seismically active areas. ISTF is a sensor web of a coordinated observation infrastructure employing multiple sensors that are distributed on one or more platforms; data from satellite sensors (Terra, Aqua, POES, DEMETER and others) and ground observations, e.g., Global Positioning System, Total Electron Content (GPS/TEC). As a theoretical guide we use the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model to explain the generation of multiple earthquake precursors. Using our methodology, we evaluated retrospectively the signals preceding the most devastated earthquakes during 2005-2011. We observed a correlation between both atmospheric and ionospheric anomalies preceding most of these earthquakes. The second phase of our validation include systematic retrospective analysis for more than 100 major earthquakes (M>5.9) in Taiwan and Japan. We have found anomalous behavior before all of these events with no false negatives. Calculated false alarm ratio for the for the same month over the entire period of analysis (2003-2009) is less than 10% and was d as the earthquakes. The commonalities in detecting atmospheric/ionospheric anomalies show that they may exist over both land and sea in regions of maximum stress (i.e., along plate boundaries) Our results indicate that the ISTF model could provide a capability to observe pre-earthquake atmospheric/ionospheric signals by combining this information into a common framework.

Ouzounov, D.↗

Real-Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR-GNSS and Deep Learning

Earthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real-time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR-GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine-Learning Assessed Rapid Geodetic Earthquake model (M-LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M-LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real-time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.

58 GEOSCIENCES↗

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗

Atmospheric Signals Associated with Major Earthquakes. A Multi-Sensor Approach

We are studying the possibility of a connection between atmospheric observation recorded by several ground and satellites as earthquakes precursors. Our main goal is to search for the existence and cause of physical phenomenon related to prior earthquake activity and to gain a better understanding of the physics of earthquake and earthquake cycles. The recent catastrophic earthquake in Japan in March 2011 has provided a renewed interest in the important question of the existence of precursory signals preceding strong earthquakes. We will demonstrate our approach based on integration and analysis of several atmospheric and environmental parameters that were found associated with earthquakes. These observations include: thermal infrared radiation, radon! ion activities; air temperature and humidity and a concentration of electrons in the ionosphere. We describe a possible physical link between atmospheric observations with earthquake precursors using the latest Lithosphere-Atmosphere-Ionosphere Coupling model, one of several paradigms used to explain our observations. Initial results for the period of2003-2009 are presented from our systematic hind-cast validation studies. We present our findings of multi-sensor atmospheric precursory signals for two major earthquakes in Japan, M6.7 Niigata-ken Chuetsu-oki of July16, 2007 and the latest M9.0 great Tohoku earthquakes of March 11,2011

Ouzounov, Dimitar↗

Overview of the SCEC/USGS Community Stress Drop Validation Study Using the 2019 Ridgecrest Earthquake Sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. Here, in this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.

58 GEOSCIENCES↗

Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog

Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.

58 GEOSCIENCES↗

Discrimination of Small Earthquakes and Buried Single-Fired Chemical Explosions at Local Distances (<150 km) in the Western United States from Comparison of Local Magnitude ( M L ) and Coda Duration Magnitude ( M C )

Seismologists distinguish underground nuclear explosions from more commonly occurring earthquakes using moment tensor inversion, high-frequency P/S amplitude ratios, m b :M s comparisons, and P–pP differential travel times. These methods are generally successful for large seismic events (M >3-4) well recorded at regional-to-teleseismic distances (>150 km); however, it is unclear whether they can be modified to work for small events (M <3) well recorded only at local distances (<150 km). Here, we evaluate a recently proposed, local-distance seismic source discriminant—the difference between local magnitude (M L ) and coda duration magnitude (M C )—using seismograms of earthquakes and buried, single-fired chemical explosions recorded in three regions of the western United States. The quantity M L –M C was previously found to be sensitive to source depth, effectively discriminating mine blasts, induced earthquakes, and very shallow tectonic earthquakes from deeper crustal earthquakes. In this study, we report the first evaluation of M L –M C as a depth discriminant using data from buried, single-fired explosions that, unlike the seismic sources studied earlier, are good analogs for underground nuclear explosions. We find that even when using generic, uncalibrated methods of assigning magnitudes, M L –M C separates single-fired explosions and earthquakes. The area under the receiver operating characteristic curve is 0.92 for 19 explosions and 14 earthquakes in Washington, 0.90 for 22 explosions and 90 earthquakes in Wyoming, and 0.99 for three explosions and 149 earthquakes in Nevada. Finally, M L :M C comparisons have the potential to enhance discrimination based on high-frequency P/S amplitudes ratios—which perform less well at local than regional distances—because the two metrics have complementary sensitivities.

58 GEOSCIENCES↗

A Big Problem for Small Earthquakes: Benchmarking Routine Magnitudes and Conversion Relationships with Coda Envelope-Derived Mw in Southern Kansas and Northern Oklahoma

Earthquake magnitudes are widely relied upon measures of earthquake size. Although moment magnitude (M w ) has become the established standard for moderate and large earthquakes, difficulty in reliably measuring seismic moments for small (generally M w <4) earthquakes has meant that magnitudes for these events remain plagued by a patchwork of inconsistent measurement scales. Because of this, magnitudes of small earthquakes and statistics derived from them can be biased. Furthermore, because small earthquakes are much more numerous than large ones, many applications, such as seismic hazard modeling, depend critically on analysis of events characterized by magnitudes other than M w . Therefore, to assess this problem, we apply coda envelope analysis to reliably determine moment magnitudes for a case study of small earthquakes from northern Oklahoma and southern Kansas. Not surprisingly, we find significant differences among M L , m bLg , and M w for M ~2–4 earthquakes examined here. More troublingly, we find that relations designed to convert other magnitudes to M w , which are relied upon for important applications such as seismic hazard analysis, often increase rather than decrease this bias for our dataset. In our case study, we find that converted magnitudes can result in a systematic bias sometimes exceeding 0.5 magnitude units, a difference that typically corresponds to a factor of ~3 in seismicity rate. Moreover, we find a correspondingly large bias in Gutenberg–Richter b-values, controlled primarily by inaccurate magnitude scaling in the conversion relationships. Although this study focuses on a relatively small geographic area, we can expect that similar issues exist with varying severity in other regions. Therefore, magnitudes of small earthquakes and their associated statistics, including seismicity rates and b-values, should be treated with caution.

58 GEOSCIENCES↗

Exploring Uncertainty in Moment Estimation for Small Earthquakes in Southern Nevada Using the Coda Envelope Method

Compiling source parameter estimates for small earthquakes is important both for our understanding of earthquake physics and for accurately assessing earthquake hazard. Reliable source parameter estimates are difficult to achieve for small earthquakes, in part due to our inability to accurately model the relevant physical processes at high frequencies. The coda envelope methodology developed by Mayeda and Walter (1996) and Mayeda et al. (2003) can mitigate this concern and estimate the moment of small earthquakes by determining the parameters that control the shape of the S-wave coda envelope while eliminating path effects by minimizing the scatter between seismic stations. Here, we use an open-source implementation of this technique called the Coda Calibration Tool (CCT; Barno, 2017) to calculate CCT-based moment magnitude estimates of small earthquakes (M L 0–3) in the Rock Valley, Nevada, region within the Nevada National Security Site. The Rock Valley data set is of particular interest because it allows us to explore the changes in uncertainties of the coda calibration method with earthquake size and depth. We found that a consistent linear relationship exists between the local magnitude M L and our coda-derived M w estimates for earthquakes as small as M L 0–3, but that current CCT workflows do not accurately characterize very shallow events. We also demonstrate that the epistemic uncertainty in the apparent stress value assumed by the CCT algorithm can influence magnitude estimates of small earthquakes. In conclusion, these results provide valuable insight into the seismicity of this region, and inform future analysis and modeling efforts for nuclear monitoring and seismic hazard.

58 GEOSCIENCES↗

Distinguishing Unique Earthquakes with Overlapping Signals in Oklahoma

Abstract During routine operations monitoring Oklahoma earthquakes, we found that certain earthquakes occurred closely both in space and time and had overlapping phases at the recording stations. Through further scrutiny and analysis, we determined that rather than being distinctly different earthquakes, some of the earthquakes exhibited multiphase arrivals and longer than expected coda due to unique ray paths that encounter impedance contrasts such as at the sedimentary rock-basement. Of course, some of these events truly were distinct events, which we term overlapping earthquakes, for which perceived coda duration overlaps and obscures the phase arrivals of the second event due to the source proximity in both time and space. We detail our classification scheme to separate the local earthquakes in Oklahoma as single, overlapping earthquakes, or those associated with multiphase arrivals. We forward model seismic wave propagation in a 2D crustal model and develop a methodology that utilizes waveform correlation to distinguish phases from overlapping earthquakes to those from crustal reverberations. Duration analysis shows a more elongated duration, qualitatively similar to the duration produced by overlapping earthquakes, at the sites where multiphase arrivals are observed.

Geochemistry & Geophysics↗

Crustal Deformation in Southcentral Alaska: The 1964 Prince William Sound Earthquake Subduction Zone

This article, for Advances in Geophysics, is a summary of crustal deformation studies in southcentral Alaska. In 1964, southcentral Alaska was struck by the largest earthquake (moment magnitude 9.2) occurring in historical times in North America and the second largest earthquake occurring in the world during the past century. Conventional and space-based geodetic measurements have revealed a complex temporal-spatial pattern of crustal movement. Numerical models suggest that ongoing convergence between the North America and Pacific Plates, viscoelastic rebound, aseismic creep along the tectonic plate interface, and variable plate coupling all play important roles in controlling both the surface and subsurface movements. The geodetic data sets include tide-gauge observations that in some cases provide records back to the decades preceding the earthquake, leveling data that span a few decades around the earthquake, VLBI data from the late 1980s, and GPS data since the mid-1990s. Geologic data provide additional estimates of vertical movements and a chronology of large seismic events. Some of the important features that are revealed by the ensemble of studies that are reviewed in this paper include: (1) Crustal uplift in the region that subsided by up 2 m at the time of the earthquake is as much as 1 m since the earthquake. In the Turnagain Arm and Kenai Peninsula regions of southcentral Alaska, uplift rates in the immediate aftermath of the earthquake reached 150 mm/yr , but this rapid uplift decayed rapidly after the first few years following the earthquake. (2) At some other locales, notably those away the middle of the coseismic rupture zone, postseismic uplift rates were initially slower but the rates decay over a longer time interval. At Kodiak Island, for example, the uplift rates have been decreasing at a rate of about 7mm/yr per decade. At yet other locations, the uplift rates have shown little time dependence so far, but are thought not to be sustainable throughout the several hundred year recurrence time for great earthquake. The nearly 10 mm/yr uplift rate at Seldovia on the Kenai Peninsula is an example.

Cohen, Steven C.↗

Basement Fault Activation before Larger Earthquakes in Oklahoma and Kansas

Oklahoma and Kansas experienced unprecedented seismic activity over the past decade due to earthquakes associated with unconventional hydrocarbon development. The modest natural seismicity and incomplete knowledge of the fault network in the region made it difficult to anticipate the locations of earthquakes with larger magnitudes (M w ≥ 4). Here, we show that monitoring of microearthquakes at regional scale using a pretrained neural phase picker and an earthquake relocation algorithm can illuminate unknown fault structures, and deliver information that can be synthesized for earthquake forecasting. We found that 80% of the larger earthquakes that occurred in the past decade could have been anticipated based on the spatial extent of the seismicity clusters that were formed before these earthquakes occurred. We also found that once a seismicity cluster with a length scale enough to host a larger earthquake was formed, there was a ~5% chance that it would host one or more larger earthquakes within a year. This probability is nearly an order of magnitude higher than one based on Gutenberg–Richter statistics and preceding seismicity. Applying our approach in practice can provide critical information on seismic hazards for risk management and regulatory decision making.

58 GEOSCIENCES↗

Coupling of regional geophysics and local soil-structure models in the EQSIM fault-to-structure earthquake simulation framework

Accurate understanding and quantification of the risk to critical infrastructure posed by future large earthquakes continues to be a very challenging problem. Earthquake phenomena are quite complex and traditional approaches to predicting ground motions for future earthquake events have historically been empirically based whereby measured ground motion data from historical earthquakes are homogenized into a common data set and the ground motions for future postulated earthquakes are probabilistically derived based on the historical observations. This procedure has recognized significant limitations, principally due to the fact that earthquake ground motions tend to be dictated by the particular earthquake fault rupture and geologic conditions at a given site and are thus very site-specific. Historical earthquakes recorded at different locations are often only marginally representative. There has been strong and increasing interest in utilizing large-scale, physics-based regional simulations to advance the ability to accurately predict ground motions and associated infrastructure response. However, the computational requirements for simulations at frequencies of engineering interest have proven a major barrier to employing regional scale simulations. In a U.S. Department of Energy Exascale Computing Initiative project, the EQSIM application development is underway to create a framework for fault-to-structure simulations. This framework is being prepared to exploit emerging exascale platforms in order to overcome computational limitations. This article presents the essential methodology and computational workflow employed in EQSIM to couple regional-scale geophysics models with local soil-structure models to achieve a fully integrated, complete fault-to-structure simulation framework. Here, the computational workflow, accuracy and performance of the coupling methodology are illustrated through example fault-to-structure simulations.

97 MATHEMATICS AND COMPUTING↗

Relocation of the 8 September 2023 High Atlas, Morocco, Earthquake Aftershock Sequence

The earthquake that occurred on 8 September 2023, with a magnitude of 6.8, was the most destructive earthquake event in Morocco in the past decade. This earthquake took place in the Al Haouz region, located in the western part of the High Atlas Mountain range. To better understand what caused and triggered this earthquake, the earthquake catalogs including P and S arrival times were collected from the Moroccan seismic network and combined with regional data from the International Seismological Centre. The mainshock and aftershocks were relocated by using iLoc, a state-of-the-art single-event location algorithm, and then by the multiple event location double-difference algorithm, hypoDD. The improved earthquake relocations using iLoc and the double-difference methods provide sharper lineation of seismicity and agree well with tomographic images of the earthquake zone. Finally, the seismicity distribution and the focal mechanism of the mainshock indicate that the earthquake sequence has occurred along the South Atlas fault system.

58 GEOSCIENCES↗

Stress-Drop and Source Scaling of the 2019 Ridgecrest, California, Earthquake Sequence

Stress drop, while difficult to measure reliably and at scale, is a key source parameter for understanding the earthquake rupture process and its relationship to strong ground motion. Here, we use a P-wave spectral decomposition approach, designed for large and densely sampled datasets, to measure earthquake stress drop in the region surrounding the 2019 Ridgecrest, California, earthquake sequence. With more than 11,000 measurements of earthquake stress drop in the 20-yr time period from 2000 through 2019, this dataset provides an opportunity to understand how coseismic stress changes and how other geophysical factors relate to the distribution of stress drop and its evolution in space and time. We observe a mild but persistent deviation from self-similar scaling, with larger events having systematically higher stress drops, though this trend depends on the assumption of an omega-square source spectral model. Earthquake stress drop increases with hypocentral depth in this study region, and the Ridgecrest aftershocks tend to have higher stress drops than the pre-event seismicity. This is in part due to their deeper hypocenters. Coherent spatial patterns of stress drop in the aftershock sequence correlate with the slip distribution of the M 7.1 mainshock, whose northwest rupture tip terminated in a long-lived zone of enervated stress drop. Although physical interpretation of these results is complicated by the trade-offs between the timing, depth, and location of these earthquakes, the observations provide new insight into the physics of the earthquake source in an area of renewed seismic activity in southern California.

58 GEOSCIENCES↗