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136 records · Page 2Linked to original sources
USGS2026

Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances

Identifying effective solutions for locating groundwater resources and ensuring the quality of drinking water is increasingly urgent, given the challenges posed by climate change and population growth. This review investigates electromagnetic geophysical imaging techniques, in both time- and frequency-domain, that can provide valuable insights for groundwater assessment. We explore computational electromagnetic methods used to evaluate electromagnetic data and several recent hydrogeophysical case studies. As open-source frameworks for modeling electromagnetic geophysical problems become available, a broader range of researchers can interpret their data with computationally advanced software. We provide an overview of documented open-source codes for evaluating electromagnetic data and analyze various hydrological targets in relation to their electromagnetic surveying technique and the computational method applied. Furthermore, we evaluate the potential of advanced computational techniques, including three-dimensional modeling, non-deterministic inversion and machine learning, to couple geophysical with numerical groundwater modeling and apply it in groundwater system studies. Despite obstacles such as complexity and resource demands, our findings indicate that the quantification and integration of predictive uncertainties from both electromagnetic and hydrological data and simulations would significantly improve the reliability of hydrogeophysical models. This can lead to a deeper understanding of groundwater systems and improved management practices.

Journal of Hydrology↗
USGS2026

Computing flow-field distortion coefficients from well-construction and formation properties

Direct measurements of groundwater velocity made with borehole flowmeters in screened wells must be compensated for the effects of flow-field distortion (also known as borehole acceleration). A theoretical equation developed by Drost et al. (1968) and simple inputs describing hydraulic properties of well construction and geologic formation were programmed into an Excel workbook to facilitate computation by groundwater-flowmeter users. Tables describing the physical and hydraulic properties for well constructions and gravel pack media are provided with an example to facilitate use of the workbook. Groundwater flowlines converge or diverge as they pass from a geologic formation, through a gravel pack and well screen. The extent of flowline convergence or divergence and the value of the flow-field distortion coefficient is related to the relative changes in hydraulic conductivity of the well screen, gravel pack, and geologic formation. Convergence or divergence is accompanied by acceleration or deceleration of groundwater. Direct measurements of groundwater velocity at the center of the monitoring well can be adjusted to provide a more accurate estimate of velocity in the formation by applying a correction for flow-field distortion. Variables required to compute the flow-field distortion coefficient include the hydraulic conductivity of the gravel pack, well screen, and the geologic formation surrounding the well screen; the borehole radius, and the inside radius and outside radius of the well screen.

Groundwater↗
USGS2024

Recent advances in characterizing the crustal stress field and future applications of stress data: Perspectives from North America

The stress field controls patterns of crustal deformation, including which faults are likeliest to cause earthquakes or transmit fluids. Since the 1950s, maps of maximum horizontal stress ( S Hmax ) orientations have advanced dramatically, and the style of faulting (relative principal stress magnitudes) has recently been mapped in some regions as well. This perspectives paper summarizes developments in characterizing stress orientations and (relative) magnitudes, including new seismic and borehole methods, as well as progress in identifying the causes of stress variations. Despite these advances, adding far more spatiotemporal detail would allow geoscientists to address many of today's key challenges regarding natural hazards, energy development, and geodynamics. In particular, it is critically important to characterize stress heterogeneity at multiple scales while also recognizing the coherent variability of the stress field. The second part of the paper considers how more detailed stress datasets could prove essential to addressing some of the grand questions in geoscience, including deciphering the poorly understood feedbacks between crustal dynamics and surface processes, improving earthquake and eruption forecasts, and determining the origins and shared properties of plate boundaries.

Geological Society, London, Special Publications↗
USGS2024

Estimating groundwater level records using MOVE.1 and computing monthly percentiles from estimated groundwater records in Massachusetts

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, performed record extensions on groundwater levels at select wells using the Maintenance of Variance Extension type 1 (MOVE.1) method. The groundwater levels estimated from these record extensions were used to compute monthly percentiles to improve future determinations of a groundwater index. In Massachusetts, 27 of 29 short-record study wells with continuous groundwater levels between 0.8 and 8.1 years were suitable for record extensions; 37 long-record index wells were used to extend the groundwater level records at the study wells. The index well selected to pair with a study well was chosen based on Pearson correlation coefficient values; cross-correlation between the two wells; geologic and topographic similarity; and smallest distance spanning the wells. Each study well and its corresponding index well have 1 or more years of concurrent, overlapping data; a Pearson correlation coefficient that exceeded a threshold value of 0.8; and a similar aquifer type and hydrologic characteristics. Of the 29 study wells, 2 showed poor correlations with all index wells and were not considered for record extensions. Performance metrics used to assess the accuracy of the MOVE.1 models indicated that most models provided reasonable estimates of groundwater levels. Root mean square error values ranged from 0.097 to 2.292 feet, with a median of 0.536 foot. Nash-Sutcliffe efficiency coefficient values ranged from 0.623 to 0.996, with a median value of 0.759. Generally, study wells in close geographical proximity to their index well resulted in stronger model performance. The average length of groundwater level records was extended by 14.1 years to a new average of 18.1 years. The estimated groundwater level records from the MOVE.1 models resulted in an increase in the range of highest and lowest groundwater levels at 23 of 27 wells. The increase in range of groundwater levels was between 0.08 to 7.95 feet. Monthly percentiles for State drought indices were computed from the estimated MOVE.1 records and observed records through December 31, 2021. Percentiles computed from estimated records show an average groundwater level about 1.0 foot lower than observed data at the 2d percentile and 0.1 foot lower at the 30th percentile.

Massachusetts↗
USGS2025

Critical Minerals in Ores (CMiO) database

Critical minerals are commodities essential to modern industrial and strategic technologies and are highly vulnerable to supply chain disruption. The Critical Minerals Mapping Initiative (CMMI) is a collaboration among the U.S. Geological Survey (USGS), the Geological Survey of Canada, and Geoscience Australia that aims to deepen global understanding of where critical minerals are located. A key output of this initiative is the Critical Minerals in Ores (CMiO) database that is advancing our collective understanding of critical minerals distributions. For instance, publicly available data on the concentrations of many critical minerals are sparse because these commodities can only be produced in small, yet essential, quantities compared to the primary commodities like copper and zinc. The CMiO database helps bridge this gap by offering high-quality, multielement geochemical data from a wide variety of critical mineral-bearing deposits around the world. Importantly, it uses a novel consensus deposit environment, group, and type classification scheme developed by the agencies that allows comparisons among ore deposits from different regions. The CMiO database contains geochemical data for more than 20,000 samples from more than 100 deposit types comprising 10 deposit environments.

Fact Sheet↗
USGS2026

USGS Geochron—A database of geochronological and thermochronological dates and data—Technical documentation

Geochronological and thermochronological data are essential for constraining the timing and rates of geological processes, supporting geologic mapping, natural hazard assessment, and resource exploration. The U.S. Geological Survey (USGS) Geochron Database is a centralized, relational database that integrates USGS and State geological survey data in accordance with the National Geologic Mapping Act of 1992 and its reauthorizations. This report documents the structure and development of the database, including its standardized schema, controlled vocabularies, and protocols for compiling legacy and newly published data. The database is designed to adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and currently (2026) includes data from a wide range of geochronological and thermochronological methods. Public access is provided through the USGS Geochron Database Explorer, a browser-based geographic information system (GIS) interface, and through versioned data releases available on ScienceBase, which provide sample-level summary data and detailed analytical information. The USGS Geochron Database is structured to be extensible, supporting the inclusion of additional methods and data types as they are compiled. This format ensures long-term utility and aligns with the Mapping Act’s directive to create a national archive of geochronological information that adds interpretive value to geologic map data. Its design reflects a commitment to data transparency, scientific reproducibility, and national geoscience priorities.

Data Report↗
USGS1976

Effect of deicing chemicals on the hydrologic environment in Massachusetts; evaluation of surface-water data collection

The objective of this study is to develop predictive relationships that can be used to describe the impact of highway deicing salts on the hydrologic environment. A method, presented in an earlier report, for estimating yearly mean chloride concentrations from estimated or actual runoff and salt-application data was applied to the period 1971 through1974 and the results compared with yearly mean chloride concentrations computed from records of specific conductance. Estimates were found to be from minus 63 percent to plus 56 percent in error when the error was expressed as the difference between estimated and computed values as a percentage of the computed values. Chloride concentrations used in this study are computed from specific conductance/chloride concentration relationships and records of specific conductance. Preliminary results from graphic analyses and least-squares regression analysis show that relationships between measured values of specific conductance and chloride concentration have correlation coefficients ranging from 0.30 to 0.97. Analysis of streamflow for all major dissolved constituents is recommended with the purpose of attempting to describe the variations in the specific conductance/chloride concentration relationships.

Massachusetts↗
USGS2025

Total uncertainty quantification in inverse solutions with deep learning surrogate models

We propose an approximate Bayesian method for quantifying the total uncertainty in inverse partial differential equation (PDE) solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations, PDE, and surrogate models. First, we use the surrogate model to formulate a minimization problem in the reduced space for the maximum a posteriori (MAP) inverse solution. Then, we randomize the MAP objective function and obtain samples of the posterior distribution by minimizing different realizations of the objective function. We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a nonlinear diffusion equation with an unknown space-dependent diffusion coefficient. Among other applications, this equation describes the flow of groundwater in an unconfined aquifer. Depending on the training dataset and ensemble sizes, the proposed method provides similar or more descriptive posteriors of the parameters and states than the iterative ensemble smoother method. Deep ensembling underestimates uncertainty and provides less-informative posteriors than the other two methods. Our results show that, despite inherent uncertainty, surrogate models can be used for parameter and state estimation as an alternative to the inverse methods relying on (more accurate) numerical PDE solvers.

Journal of Computational Physics↗
USGS2026

SlideDetect: Spatio-temporal landslide detection using a three-dimensional convolutional neural network

Landslides pose a serious and ongoing threat to both human lives and infrastructure worldwide; therefore, it is of interest to predict where and when landslides are likely to occur. Advances in machine learning techniques have spurred numerous studies aimed at estimating relative landslide propensity, but are limited to spatial (as opposed to temporal) prediction due to the sparsity of landslide timing data. We address this data gap by training SlideDetect, a 3-dimensional convolutional neural network (3D CNN), to identify landslides based on their spatial and temporal occurrence within multitemporal image stacks. We use an inventory of landsides triggered by the 2018 Hokkaido earthquake and two years of monthly composite optical imagery spanning this event. The model can identify not only landslide location but also landslide date with an area under the precision-recall curve (PR-AUC) of 0.84. We further present a new standard for presenting PR curve results that explicitly compares model performance at different confidence thresholds, allowing for clearer model evaluation and comparison. Our new approach to constraining landslide timing paired with this more consistent and objective method for evaluating model performance shows considerable promise, and with further application and testing, SlideDetect could enhance the data availability and tools needed to advance landslide hazard and risk assessments.

JGR Machine Learning and Computation↗
USGS2026

Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system

Computation of detailed groundwater flow budgets for subdivisions of Virginia’s Coastal Plain aquifer system has enabled quantification and more thorough understanding of groundwater flow within this important water resource. A zone budget analysis conducted on previously published groundwater models of the Virginia Coastal Plain and Virginia Eastern Shore shows that groundwater conditions vary substantially throughout the Coastal Plain aquifer system due to local variations in hydrogeology and historical and ongoing variations in groundwater use and management. Decades of substantial groundwater withdrawal from the Coastal Plain aquifer system have fundamentally altered groundwater flow from pre-development conditions. Rates of sustainable withdrawal are limited because the downward groundwater flow rate into confined aquifers supplying groundwater is a relatively small portion of the total groundwater water budget for the aquifer system. Analyses of groundwater budgets from the Virginia Coastal Plain model show that groundwater flow is generally outward from the surficial aquifer to rivers and coastal water bodies and downward through a series of underlying aquifers and confining units to the Potomac aquifer, which is the deepest aquifer and the source of most groundwater withdrawals. Downward flow into the Potomac aquifer currently is estimated to be only 7 percent of total net precipitation-derived net recharge at the land surface but makes up about 66 percent of inflow to the aquifer in Virginia, with much of the remaining inflow occurring laterally from areas outside of defined groundwater budget regions in Virginia. For several decades prior to 2010, high rates of withdrawal from the Potomac aquifer resulted in substantial decline in groundwater storage in the aquifer and in most overlying aquifers and confining units. From 2010 to 2025, rates of withdrawal substantially lower than the historical maximum have resulted in small net increases in groundwater storage in the confined aquifer system for most regions of the Virginia Coastal Plain. Nevertheless, for the same period, groundwater storage for the entire model domain continues to incrementally decline, indicating that storage recovery in Virginia is offset by a continued decrease in storage in areas beneath the Chesapeake Bay or in adjacent areas of Maryland and North Carolina. Withdrawals from the Potomac aquifer have induced substantial downward flow which is a large part of groundwater budgets for confined aquifers such as the Potomac. Downward groundwater flow continues under current conditions, but because vertical flow rates are a function of the difference between water pressure in the upper surficial systems and lower confined units, those rates are lower than those in earlier decades as the confined water levels partially recover from larger groundwater withdrawals in the past. Geographically, groundwater flow is generally inward from perimeter regions of the Virginia Coastal Plain toward central regions with the largest withdrawal rates. Estimated groundwater inflow from coastal regions could be contributing to saltwater intrusion, though that was not measured directly in this study. Analyses of groundwater budgets from the Virginia Eastern Shore peninsula, a geographic region of the Virginia Coastal Plain, show that groundwater flow for that isolated aquifer system is generally outward from the surficial aquifer to coastal water bodies and downward into the confined Yorktown-Eastover aquifer system, which is the source of most withdrawals. Downward groundwater flow into the confined Yorktown-Eastover aquifer system is estimated to be less than 2 percent of total recharge and less than 9 percent of net recharge at the water table but makes up over 93 percent of all inflow to the confined aquifer system. Decades of substantial but relatively consistent groundwater withdrawals have induced greater downward flow rates into the confined aquifer system but also have resulted in loss of groundwater from storage. Currently, estimated storage loss accounts for slightly under 7 percent of withdrawals from the confined aquifer system. The current withdrawal rate from the confined Yorktown-Eastover system is near the highest reported rate for the Eastern Shore, which means that the storage depletion is expected to continue, even though groundwater levels appear to be relatively stable. Estimated groundwater flow rates upward from the confining unit underlying the Yorktown-Eastover system and small rates of inflow from coastal water bodies underscore ongoing concerns about up-coning and lateral intrusion of salty groundwater.

EarthArXiv↗
USGS2026

Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system

Computation of detailed groundwater flow budgets for subdivisions of the Virginia Coastal Plain aquifer system has enabled quantification and more thorough understanding of groundwater flow within this important water resource. A zone budget analysis based on previously published groundwater models of the Virginia Coastal Plain and Virginia Eastern Shore indicates that groundwater conditions vary substantially throughout the Coastal Plain aquifer system because of local variations in hydrogeology and historical and ongoing variations in groundwater use and management. Decades of substantial groundwater withdrawal from the Coastal Plain aquifer system have altered groundwater flow from predevelopment conditions. Rates of sustainable withdrawal are limited because the downward groundwater flow rate into confined aquifers is a relatively small part of the total groundwater budget for the aquifer system compared to the rate of recharge at the land surface. Analyses of groundwater budgets from the Virginia Coastal Plain model indicate that groundwater flow is generally outward from the surficial aquifer to rivers and coastal waterbodies and downward through a series of underlying aquifers and confining units to the Potomac aquifer, which is the deepest aquifer and the source of most groundwater withdrawals. Downward flow into the Potomac aquifer is estimated to be only 7 percent of total net precipitation-derived net recharge at the land surface but makes up about 66 percent of inflow to the aquifer in Virginia, with much of the remaining inflow occurring laterally from outside of defined groundwater budget regions in Virginia. For several decades prior to 2010, high rates of withdrawal from the Potomac aquifer resulted in substantial decline in groundwater storage in the aquifer and in most overlying aquifers and confining units. From 2010 to 2023, rates of withdrawal substantially lower than the historical maximum resulted in small net increases in groundwater storage in the confined aquifer system for most regions of the Virginia Coastal Plain. Nevertheless, for the same period, groundwater storage for the entire model domain continues to incrementally decline, indicating that storage recovery in Virginia is offset by a continued decrease in storage in areas beneath the Chesapeake Bay or adjacent areas of Maryland and North Carolina. Withdrawals from the Potomac aquifer have induced substantial downward flow which is a large part of groundwater budgets for confined aquifers such as the Potomac. For the most recent simulated conditions (2023) downward groundwater flow continues, but because vertical flow rates are a function of the difference between water pressure in the upper surficial systems and lower confined units, rates of downward flow are lower than those in earlier decades as the confined water levels partially recover from larger groundwater withdrawals in the past. Geographically, groundwater flow is generally inward from perimeter regions of the Virginia Coastal Plain toward central regions with the largest withdrawal rates. Groundwater inflow from coastal regions could be contributing to saltwater intrusion, even though that was not measured in this study. Analyses of groundwater budgets from the Virginia Eastern Shore peninsula, a geographic region of the Virginia Coastal Plain, indicate that groundwater flow for that isolated aquifer system is generally outward from the surficial aquifer to coastal water bodies and downward into the confined Yorktown-Eastover aquifer system, which is the source of most withdrawals. Downward groundwater flow into the confined Yorktown-Eastover aquifer system is estimated to be less than 2 percent of total recharge and less than 9 percent of net recharge at the water table but makes up more than 93 percent of all inflow to the confined aquifer system. Decades of substantial but relatively consistent groundwater withdrawals have induced greater downward flow rates into the confined aquifer system but also have resulted in loss of groundwater from storage. For the most recent simulated period (2023), estimated storage loss accounts for slightly under 7 percent of withdrawals from the confined aquifer system. The reported withdrawal rate for this period from the confined Yorktown-Eastover system is near the highest reported rate for the Virginia Eastern Shore, which means that the storage depletion is expected to continue, even though groundwater levels appear to be relatively stable. Estimated groundwater flow rates upward from the confining unit underlying the Yorktown-Eastover system and low rates of inflow from coastal water bodies underscore ongoing concerns about up-coning and lateral intrusion of salty groundwater.

Virginia↗
USGS2026

Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography

Self-potential (SP) monitoring has emerged as a valuable method for characterizing subsurface hydrogeological features and processes due to its sensitivity to fluid-induced electrokinetic effects. Despite advancements in SP inversion, challenges remain in imaging groundwater dynamics from SP activities due to complex hydrological settings and transient noise. In this study, a deep learning autoencoder (AE)-based framework is proposed for the spatiotemporal localization of subsurface fluid movement from time-lapse SP tomography. Temporal segments of time-lapse numerical inversions were first derived from long-term SP monitoring conducted from a floodplain site in Oak Ridge, Tennessee, known for active hyporheic exchange. Subsequently, AE models based on vision transformer (ViT), convolutional long short-term memory (ConvLSTM), convolutional neural network, and temporal convolutional network were individually trained and compared on the SP tomography segments for reconstruction performance. Finally, the reconstruction error over time serves as an anomaly score to identify moments of active SP variation, whereas spatial distributions of errors within these moments are analyzed to image and localize regions associated with anomalous subsurface fluid movement. The results demonstrate that ConvLSTM- and ViT-AE are most capable for the localization task with contrasting error distributions and consistent delineation of anomalies. Applying the method to both SP arrays parallel and perpendicular to the stream produced consistent anomaly zones near a fault or karst feature, validating the robustness and generalization of the approach. These results demonstrate the potential of the proposed framework as a scalable and interpretable tool for spatiotemporal analysis of subsurface flow dynamics in complex hydrogeological systems.

Tennessee↗
USGS1997

Application of acoustical methods for estimating water flow and constituent loads in Perdido Bay, Florida

Water flow and quality data were collected from December 1994 to September 1995 to evaluate variations in discharge, water quality, and chemical fluxes (loads) through Perdido Bay, Florida. Data were collected at a cross section parallel to the U.S. Highway 98 bridge. Discharges measured with an acoustic Doppler current profiler (ADCP) and computed from stage-area and velocity ratings varied roughly between + or - 10,000 cubic feet per second during a typical tidal cycle. Large reversals in flow direction occurred rapidly (less than 1 hour), and complete reversals (resulting in near peak net-upstream or downstream discharges) occurred within a few hours of slack water. Observations of simultaneous upstream and downstream flow (bidirectional flow) were quite common in the ADCP measurements, with opposing directions of flow occurring predominantly in vertical layers. Continuous (every 15 minutes) discharge data were computed for the period from August 18, 1995, to September 28, 1995, and filtered daily mean discharge values were computed for the period from August 19 to September 26, 1995. Data were not computed prior to August 18, 1995, either because of missing data or because the velocity rating was poorly defined (because of insufficient data) for the period prior to landfall of hurricane Erin (August 3, 1995). The results of the study indicate that acoustical techniques can yield useful estimates of continuous (instantaneous) discharge in Perdido Bay. Useful estimates of average daily net flow rates can also be obtained, but the accuracy of these estimates will be limited by small rating shifts that introduce bias into the instantaneous values that are used to compute the net flows. Instantaneous loads of total nitrogen ranged from -180 to 220 grams per second for the samples collected during the study, and instantaneous loads of total phosphorous ranged from -10 to 11 grams per second (negative loads indicate net upstream transport). The chloride concentrations from the water samples collected from Perdido Bay indicated a significant amount of mixing of saltwater and freshwater. Mixing effects could greatly reduce the accuracy of estimates of net loads of nutrients or other substances. The study results indicate that acoustical techniques can yield acceptable estimates of instantaneous loads in Perdido Bay. However, estimates of net loads should be interpreted with great caution and may have unacceptably large errors, especially when saltwater and freshwater concentrations differ greatly.

Florida↗
USGS2026

Bathymetric maps, surface areas, and storage capacities of Council Grove Lake and Marion Reservoir, Kansas, and Pine Creek Lake, Oklahoma, 2024

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed high-resolution multibeam bathymetric surveys to compute new elevation-area-capacity tables for Council Grove Lake and Marion Reservoir, Kansas, and Pine Creek Lake, Oklahoma. Elevation-area-capacity tables identify the relation between the water-surface elevation, surface area, and storage capacity of the lake. The surface areas and storage capacities of each lake were computed from bathymetric surfaces combining multibeam echo sounder data collected in 2024 and light detection and ranging point-cloud data collected in 2016 and 2018.

Kansas, Oklahoma↗
USGS1997

An optimized network for phosphorus load monitoring for Lake Okeechobee, Florida

Phosphorus load data were evaluated for Lake Okeechobee, Florida, for water years 1982 through 1991. Standard errors for load estimates were computed from available phosphorus concentration and daily discharge data. Components of error were associated with uncertainty in concentration and discharge data and were calculated for existing conditions and for 6 alternative load-monitoring scenarios for each of 48 distinct inflows. Benefit-cost ratios were computed for each alternative monitoring scenario at each site by dividing estimated reductions in load uncertainty by the 5-year average costs of each scenario in 1992 dollars. Absolute and marginal benefit-cost ratios were compared in an iterative optimization scheme to determine the most cost-effective combination of discharge and concentration monitoring scenarios for the lake. If the current (1992) discharge-monitoring network around the lake is maintained, the water-quality sampling at each inflow site twice each year is continued, and the nature of loading remains the same, the standard error of computed mean-annual load is estimated at about 98 metric tons per year compared to an absolute loading rate (inflows and outflows) of 530 metric tons per year. This produces a relative uncertainty of nearly 20 percent. The standard error in load can be reduced to about 20 metric tons per year (4 percent) by adopting an optimized set of monitoring alternatives at a cost of an additional $200,000 per year. The final optimized network prescribes changes to improve both concentration and discharge monitoring. These changes include the addition of intensive sampling with automatic samplers at 11 sites, the initiation of event-based sampling by observers at another 5 sites, the continuation of periodic sampling 12 times per year at 1 site, the installation of acoustic velocity meters to improve discharge gaging at 9 sites, and the improvement of a discharge rating at 1 site.

Florida↗
USGS2025

3D Converted wave reverse time migration imaging

We describe a newly developed method for recovering high-resolution images of seismic discontinuities, such as subducting slabs, in 3D. Our method makes use of converted S → P or P → S waves observed by dense arrays of seismometers to infer the locations and relative strengths of seismic discontinuities at depth in a target region. Observed direct and converted waves are backpropagated to their times of origin. The time-reversed wavefield is then separated into its constituent P and S components via the Helmholtz decomposition, and those separated wavefields are used to compute imaging functions that characterize the locations and relative strengths of seismic discontinuities. Imaging functions may be designed to use either S → P or P → S waves, so that users can target those arrivals expected to be most dominant in a given dataset. We have previously demonstrated the efficacy of our method in two dimensions, and we now present a 3D implementation of our technique which addresses the significant computational challenges posed by the size of volumetric wavefield data in three dimensions. Through a series of synthetic examples, we demonstrate that our method is capable of recovering the fine scale structure of a subducting slab given realistic station coverage and earthquake sources. We investigate optimal seismic station geometries for our technique and explore image interpretability in regions with poor data coverage. We find that linear station geometries yield more optimal, interpretable imaging functions than collections of small arrays can. We also show that our method can successfully recover bothS → P or P → S images when realistic shear earthquake sources are used, and we explore the additional computational challenges presented by the high frequency content of S waves. Our results demonstrate the potential for our technique to recover high-resolution information about subducting slabs in real-world regions, given that relatively sparse seismic arrays with only approximately 100 stations are capable of recovering interpretable imaging functions from just a few realistic earthquake sources for multiple discontinuities at significant depth in an area of approximately 400~sq~km.

Seismica↗
USGS2026

Efficient physics‐informed ground‐motion simulations with reduced‐order models: CyberShake implications and high‐resolution site terms for southern San Andreas fault earthquakes

Recent advances in Probabilistic Seismic Hazard Analysis (PSHA) leverage physics‐based ground‐motion simulations to estimate seismic hazard, such as the CyberShake project. However, computational costs quickly escalate when performing PSHA for numerous faults or sites and can become prohibitively expensive. To reduce computational demands, CyberShake uses reciprocity and interpolates physics‐informed corrections from simulations conducted at fewer locations, but the accuracy of these interpolations remains poorly quantified. To quantify the interpolation accuracy, we derive high‐resolution, frequency‐dependent site terms for southern California and compare them with interpolated site terms using the CyberShake approach. We accomplish this by performing a set of earthquake point‐source simulations distributed along the nonplanar fault geometry for the southern San Andreas fault (SSAF) extending from Bombay Beach to Lake Hughes. Using SeisSol, we simulate three minutes of viscoelastic seismic wave propagation for these sources and store the horizontal‐component Green’s functions for 480,000 sites. We then use a scientific machine learning approach based on interpolated proper orthogonal decomposition to construct an accurate reduced‐order model of the Green’s functions to efficiently predict effective amplitude spectra (EAS) for finite‐source rupture models of SSAF earthquakes. Using minimum curvature interpolation with tension, as used in CyberShake, we compare the interpolated site terms against our high‐resolution site terms. We identify local discrepancies with EAS differing by up to a factor of approximately three. Furthermore, we identify locations where unexpectedly high or low ground motions are missed when using the interpolated dataset for these earthquakes. We estimate that our approach may be used within CyberShake to reduce the time‐to‐solution by a factor of 336 for the entire earthquake rupture forecast. Our analysis of physics‐based site terms provides more insight into the seismic hazard due to SSAF ruptures and guides future developments by combining high‐performance computing and reduced‐order modeling techniques for PSHA.

California↗
USGS2025

Special issue "Effects of surface geology on seismic motion (ESG): General state-of-research"

To understand and predict the behaviors of strong ground motions incurred during devastating earthquakes, studies focused on the “effects of surface geology (ESG) on seismic motions” (following tradition, herein contracted as: “ESG”) have progressed steadily in the last three decades. These ESG studies typically involve in situ measurements of the wavefield and commonly apply both analytical and computational approaches. Concurrently, improvements in ESG related research can be readily attributed to the proliferation of openly accessible strong motion data, as well as advances in computational power. Nevertheless, there remain significant shortfalls in our understanding of the epistemic and aleatory uncertainties associated with the ESG as demonstrated by phenomena from recent deadly earthquake-related site effects. Thus, investigations toward addressing these deficiencies should be underscored in future earthquake related disaster mitigation efforts. This special issue of Earth, Planets, and Space (EPS) is dedicated to the ongoing efforts by the International Association of Seismology and Physics of the Earth’s Interior (IASPEI) and the International Association of Earthquake Engineering (IAEE) that champion activities promoting studies related to the ESG.

Earth, Planets and Space↗