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97 records · Page 4Linked to original sources
USGS2025

ARCHI: A new R package for automated imputation of regionally correlated hydrologic records

Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.

California, New York↗
USGS2025

Earthquake stress-drop values delineate spatial variations in maximum shear stress in the Japanese forearc lithosphere

Earthquake stress drop (Δσ) may increase with depth and stress in the brittle lithosphere. However, the range of uncertainty in Δσ and the lack of constraints on absolute stress make it difficult to establish whether they are correlated. Here, we investigate Δσ dependence on depth and maximum shear stress ( τ max ) based on ~11 years of seismicity in the northeastern Japanese forearc following the 2011 Tohoku-Oki megathrust earthquake. We interpret Δσ estimates computed using both individual spectra and spectral-ratio methods and find that Δσ exhibits a clear depth dependence within the seismically active upper ~60 km of the forearc lithosphere ( ~ 0.8 MPa per 10 km). We further compare Δσ values with quantitative τ max estimates from finite-element models of force balance. We find that median Δσ values increase with τ max in the brittle forearc lithosphere and that earthquake stress release is proportional to τ max . The dependence of Δσ on τ max explains the apparent depth dependence of Δσ and suggests that average Δσ values provide a relative measure of the stress at failure. In the northeastern Japanese forearc, Δσ values remained roughly constant in the decade following the Tohoku-Oki earthquake, suggesting negligible changes in failure stress in the forearc since the mainshock.

Communications Earth and Environment↗
USGS2026

Orientation dependence of probabilistic seismic hazard estimates from CyberShake physics-based simulations

Earthquake ground‐motion intensities, such as pseudospectral accelerations (SAs), can vary significantly with horizontal orientation. However, conducting probabilistic seismic hazard analysis (PSHA) for each horizontal orientation is challenging because current ground‐motion models used in PSHA consider only a single horizontal intensity value, usually the median across all orientations, known as RotD50. To address this limitation, we employ physics‐based simulations for PSHA, which contain full waveforms from which ground‐motion intensities can be computed for all horizontal orientations to study directional seismic hazard. We apply our approach to the latest CyberShake study of the Greater Los Angeles metropolitan area, developed by the Statewide California Earthquake Center, finding that seismic hazard at a 2475‐yr return period, a common value used for earthquake‐resistant design, varies significantly with horizontal orientation. For instance, for SAs at 3 s, the maximum seismic hazard across all horizontal orientations is, on average, 15% higher than the median RotD50 hazard, with these differences becoming more pronounced at longer periods. These observed variations can generally be attributed to physical mechanisms that polarize seismic waves, such as the radiation pattern of the earthquake source and the influence of the subsurface structure. These results may have important implications for earthquake engineering applications, particularly for long‐period structures in areas with substantial horizontal variations in seismic hazard.

California↗
USGS2026

Constraining source and path effects of large magnitude earthquakes using ground motion simulations

The purpose of this study is to use ground‐motion simulations to investigate ways in which source and path effects for large‐magnitude earthquakes can be represented in nonergodic ground‐motion models (GMMs). To achieve this, we designed a ground‐motion study in the San Francisco Bay Area that includes earthquakes with a broad range of magnitudes distributed uniformly on a fault plane, and sites covering a large range of rupture distances and azimuths. After running a large suite of kinematic simulations (magnitude 4–7), we then develop a nonergodic GMM with the simulated data. We find that trends in the within‐site residuals are affected significantly by the earthquake radiation pattern, rupture directivity, and slip patterns. Next, we modify an existing rupture directivity model to fit and remove the observed radiation pattern and rupture directivity from the residuals. We also minimize the contributions of slip patterns by averaging the within‐site residuals among multiple source realizations. Finally, after removing the source effects from the within‐site residuals, we compare the path effects computed with different magnitude groups using two approaches. The first approach only considers the small events that have the same shortest path to a site as the large events, whereas the second approach considers all small events on the fault plane. The results indicate that it is difficult to satisfactorily approximate the path effects of large events with those of small events using either approach, at least in the case of simulations.

California↗
USGS2024

RegionGrow3D: A deterministic analysis for characterizing discrete three-dimensional landslide source areas on a regional scale

Regional-scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics-based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three-dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad-scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km 2 ) using 3D, limit-equilibrium (LE)-based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back-calculated from inventoried landslides using 3D LE-based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

Journal of Geophysical Research: Earth Surface↗
USGS2025

Near-fault amplification and ground motion variability during the 2019 Ridgecrest, California sequence

We estimate ground-motion variability near the 2019 M 7.1 Ridgecrest earthquake sequence. Accurate seismic hazard estimation requires understanding ground-motion spatial correlations, yet many studies lack the dense station coverage needed to resolve small-scale variability. The 2019 M 7.1 Ridgecrest earthquake sequence presents a unique opportunity to examine ground motions and their spatial correlations at a range of interstation distances. The permanent seismic network was augmented with hundreds of temporary stations including several fault-crossing nodal arrays. We compute the event ( δE i ) and within-event ( δW ij ) residuals from the observed peak ground velocity and peak ground acceleration data to isolate potential sources of ground-motion variability. We then compare δW ij between station pairs that record an event to understand the semivariance of the ground motion versus interstation distance. By fitting an exponential model to the semivariances, we determine a correlation range of 25 km for the Ridgecrest region. Although the exponential model fits the broad-scale increase of semivariance with interstation distance, we also observe smaller-scale trends. We find that ground motions are less correlated for station pairs that are near or across faults that ruptured during the 2019 Ridgecrest sequence. We also find large, positive median δW ij with relative values 2–3 times larger than nearby stations for individual stations’ near-fault traces. Near-fault amplification and greater ground-motion variability can delineate fault zones and may locally increase the seismic hazard.

California↗
USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗
USGS2025

False positives in the identification of dynamic earthquake triggering

Dynamic earthquake triggering is commonly identified through the temporal correlation between increased seismicity rates and global earthquakes that are possible triggering events. However, correlation does not imply causation. False positives may occur when unrelated seismicity rate changes coincidently occur at around the time of candidate triggers. We investigate the expected false positive rate in Southern California with global M ≥ 6 earthquakes as candidate triggers. We compute the false positive rate by applying the statistical tests used by DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 to synthetic earthquake catalogs with no real dynamic triggering. We find a false positive rate of ∼3.5%–8.5% when realistic earthquake clustering is present, consistent with the 95% confidence typically used in seismology. However, when this false positive rate is applied to the tens of thousands of spatial-temporal windows in Southern California tested in DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 , thousands of false positives are expected. The expected false positive occurrence is large enough to explain the observed apparent triggering following 70% of large global earthquakes (DeSalvio & Fan, 2023, https://doi.org/10.1029/2023jb026487 ), without requiring any true dynamic triggering. Aside from the known triggering from the nearby El Mayor-Cucapah, Mexico, earthquake, the spatial and temporal characteristics of the reported triggering are indistinguishable from random false positives. This implies that best practice for dynamic triggering studies that depend on temporal correlation is to estimate the false positive rate and investigate whether the observed apparent triggering is distinguishable from the correlations that may occur by chance.

JGR Solid Earth↗
USGS2024

Parsimonious high-resolution landslide susceptibility modeling at continental scales

Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database ( N = 613,724), a high-resolution digital elevation model (10-m), and high-performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. We down-sample our results to a 90-m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories ( N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.

AGU Advances↗
USGS2026

Hazard potential of compound flooding from rainfall, storm surge, and groundwater in coastal New York and Connecticut

Compound flood events, the co-occurrence of multiple flood drivers, can result in flood hazard potential exceeding that of any single driver alone. To evaluate compound flooding in a semi-urbanized coastal area, historical records dating back to 1970 are used to study the co-occurrences of high precipitation, storm surge, and shallow groundwater conditions along the coastlines of New York and Connecticut. Joint return periods for coincident precipitation-surge events were computed using statistical dependence models and compared to the assumption of independence as a ratio, referred to here as a return period adjustment. Results indicate distinct seasonality where compound events in the area disproportionately occur in the cold season between October and April. Return period adjustments range from a factor of 1 to almost 9, demonstrating the range in precipitation-storm surge dependence across the study area. Across all 24 station triad locations, groundwater levels were elevated during times of precipitation- surge co-occurrence, reflecting the tendency for coastal storms and shallow groundwater conditions to co-occur seasonally. The result is a pseudo-trivariate compound flood hazard score and corresponding hazard map that integrates dependence between daily precipitation-surge events and overall monthly groundwater levels (as a precondition) into a relative compound hazard score. The location with the highest compound flood hazard score is on the south shore of Long Island, as well as locations across coastal Connecticut where groundwater levels compound the co-occurrence of heavy precipitation and storm surge.

Connecticut, New York↗
USGS2025

Modeling byproduct and coproduct mine production and mineral substitution using multidimensional supply curves: Application to the Cu-Co-Ni system and beyond

Rapid demand growth is expected for many metals used in the energy transition. Many of these metals are byproducts of other commodities. Byproduct production’s price response is tied to host mineral economics, complicating its supply dynamics. Moreover, many of these metals are used in applications where the material properties desired are difficult to substitute; effectively, limiting how quickly demand can adapt to changes in commodity price. Previous work has demonstrated the interconnectivity of jointly produced mineral commodities from the supply side, where the copper–cobalt–nickel system was used and demand was assumed independent across commodities. Studies to understand byproduct-coproduct market interconnectivity on the demand side are limited, while studies on the interconnectivity of supply and demand simultaneously are even more so. We propose a modification to the multicommodity supply curve method to enable inter-commodity effects on demand simultaneous with supply. In batteries, high cobaltprices may push consumers to transition to high-nickel chemistries, causing the nickel demand surface to decrease with nickel price but increase with cobaltprice, creating a two-dimensional demand surface. Below cross-price elasticities of 0.05, inter-commodity effects were found to be negligible, potentially permitting exclusion of these effects for many commodities. This additional demand curve complexity introduces potential computation challenges alongside the capacity to model many interrelated commodity systems such as rare earth elements, ferroalloys, country-oriented subsidies or restrictions, and bifurcated sustainable metals markets. By presenting the work done on multicommodity supply surfaces to date and potential new directions, this work aims to catalyze the next round of innovative approaches to modeling jointly produced commodities.

Conference Paper↗
USGS2026

Magnitude conversion relations create substantial differences in seismic hazard models

Earthquake catalogs are essential data inputs for seismic hazard modeling. Because earthquake magnitudes are reported in a variety of types (e.g., local magnitudes and moment magnitudes), magnitude conversion relationships must be used to convert the different magnitude types present in a catalog to a uniform magnitude type to avoid biases in the hazard computation. However, these conversion relationships are often uncertain and have been shown to sometimes perform poorly. Here, we investigate the sensitivity of the gridded seismicity component of the National Seismic Hazard Model (NSHM) to the catalog conversion equations in the Eastern United States. In the 2023 NSHM, magnitudes of various types were converted to moment magnitudes using equations developed by the Central and Eastern United States Seismic Source Characterization for Nuclear Facilities (CEUS‐SSCn), based on least‐squares (LS) regressions made using data from a catalog containing events up through 2008. We recompute these equations using events in the Advanced National Seismic System Comprehensive Earthquake Catalog with multiple magnitudes from 2000 to 2023. Although we prefer the use of orthogonal regressions for our datasets, LS regressions produce broadly similar results, with both approaches exhibiting large deviations from the CEUS‐SSCn conversions, especially at smaller magnitudes. We compare the spatial distribution of annual rates using three different models: (1) the 2023 NSHM conversions, (2) our updated conversions, and (3) no conversions. We find that the choice of conversions leads to substantial differences in the rate forecasts, which can greatly impact the seismic hazard model, particularly in regions with low‐seismicity rates such as the Eastern United States, where the hazard is dominated by gridded seismicity rather than a fault model.

Seismological Research Letters↗
USGS2026

Cajon Pass and the southern San Andreas Fault System: Earthquake cycle stress accumulation and present-day loading

With over a century since the last major rupture affecting the wider Los Angeles region, tectonic stress has steadily built along the southern San Andreas and San Jacinto fault systems, raising concerns of an imminent large earthquake. Cajon Pass, located at the junction of these faults, represents a critical site for potential through-going ruptures in Southern California. We constructed new 4D earthquake cycle simulations using a 1000-year paleoseismic rupture history of the San Andreas Fault System (SAFS) to assess spatial and temporal variations in stress. A semi-analytic Fourier transform model was used to compute stress from 3D dislocations in an elastic plate overlying a Maxwell viscoelastic half-space, assuming a complete coseismic reset of resolved shear stress on ruptured elements. Results show highest stress accumulation north of Cajon Pass (∼1.8 MPa/100 years) due to greater slip rates, and lower rates south of Cajon Pass (∼1.0–1.5 MPa/100 years). By 2025, Coulomb stress is estimated at 2.8 MPa on the Mojave South (MOS) segment, 1.8 MPa on the North San Bernardino (NSB1) segment and 3.6 MPa on the San Jacinto Bernardino (SJB) segment. Segments accumulate stresses with characteristic ranges of pre-event stress interpreted as failure thresholds: 1.2–2.7 MPa for MOS, 0.4–1.6 MPa for NSB1, and 1.2–2.9 MPa for SJB. When the stress disparity between segments SJB and MOS narrows, the faults appear to rupture jointly, suggesting that stress levels may control how Cajon Pass acts as an earthquake gate. These results may inform seismic hazard assessments by linking stress evolution to fault interactions.

California↗
USGS2025

Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington↗
USGS2026

Design and function of the Autonomous Benthic Imaging and Surveying System (ABISS) for remote sensing of lake and seabed environments

Lake and seabed environments are home to fisheries and other biota that are important to ecosystems and economies, yet these environments and the species that use them are difficult to accurately assess and monitor. Traditional benthic survey techniques, like bottom trawling used by the U.S. Geological Survey, are limited by substrate constraints, poor spatial resolution and precision, and operational depth limits, hindering accurate assessment of benthic species and habitats. In response to these limitations, the U.S. Geological Survey developed the Autonomous Benthic Imaging and Surveying System, a camera system integrated into underwater vehicles, to capture high-resolution images of the lakebed. The system uses color and stereo cameras to collect imagery, which can be analyzed using computational methods to detect organisms and (or) characterize habitat features, such as geologic substrate types. The system has been integrated into autonomous underwater vehicles and into an underwater housing used by self-contained underwater breathing apparatus (SCUBA) divers. Although the engineering of the system was motivated by the need for data collection in the Great Lakes, it has potential to collect high quality data in any aqueous setting with sufficient water clarity and safe operating conditions. The Autonomous Benthic Imaging and Surveying System can operate across diverse depths and light conditions to map and quantify ecological patterns that were difficult or impossible to assess using traditional methods. The Autonomous Benthic Imaging and Surveying System offers the potential for accurate and precise monitoring and assessment of native benthic biota, invasive species, and habitat, potentially providing natural resource managers with improved information to support decision making about benthic resource management.

Great Lakes↗
USGS2025

Identification of representative earthquakes for probabilistic tsunami hazard analysis (PTHA) using earthquake rupture forecasts and machine learning

As probabilistic tsunami hazard analysis (PTHA) focuses more on assessments for localized, populous regions, techniques are needed to identify a subsample of representative earthquake ruptures to make the computational requirements for producing high-resolution hazard maps tractable. Moreover, the greatest epistemic uncertainty in seismic PTHA is related to source characterization, which is often poorly defined and subjective. We address these two salient issues by applying streamlined earthquake rupture forecasts (ERFs), based on combinatorial optimization methods, to an unsupervised machine learning workflow for identifying representative ruptures. ERFs determine the optimal distribution of a millennia-scale sample of earthquakes by inverting the observed slip rate on major faults. We use two previously developed combinatorial optimization ERFs, integer programming and greedy sequential, to produce the optimal location of ruptures with seismic moments sampled from a regional Gutenberg–Richter magnitude–frequency distribution. These ruptures in turn are used to calculate peak nearshore tsunami amplitude, using computationally efficient tsunami Green's functions. An unsupervised machine learning workflow is then used to identify a small subsample of the earthquakes input to ERFs for onshore PTHA analysis. We eliminate epistemic uncertainty related to source distribution under traditional PTHA analysis; in its place, a quantifiable, less subjective and generally smaller uncertainty related to the input to ERFs is included. The Nankai subduction zone is used as a test case, where previous ERFs have been conducted. Results indicate that the locations of representative earthquakes are sensitive to choice of magnitude–area relation and to whether a minimum cumulative stress objective is imposed on the fault. In general, incorporating ERFs into PTHA provide a physically self-consistent method to incorporate fault slip information in determining representative earthquakes for onshore PTHA, eliminating a major source of epistemic uncertainty.

Nankai subduction zone↗
USGS1998

Peak-discharge frequency and potential extreme peak discharge for natural streams in the Brazos River basin, Texas

The 2-, 5-, 10-, 25-, 50-, and 100-year peak discharges were estimated for 186 streamflow-gaging stations with at least 8 years of data for natural streams in and near the Brazos River Basin, Texas. Multiple regression equations were developed to estimate peak-discharge frequency for the 2-, 5-, 10-, 25-, 50-, and 100-year recurrence intervals for each of three hydrologic regions that compose the Brazos River Basin. The equations for each region are a function of significant basin characteristics (explanatory variables). The significant explanatory variables among six that were tested are the contributing drainage area and stream slope for regions 1 and 2 and the contributing drainage area for region 3. For the three sets of equations, the coefficient of determination ranges from 0.59 to 0.93, and the standard error ranges from 0.184 to 0.391 log units. A larger coefficient of determination and a lower standard error generally are associated with the equations for hydrologic regions 2 and 3. Statistics from the regression analysis allow computation of the prediction interval associated with a given significance level for a peak-discharge frequency estimate. The regression equations can be used to estimate peak discharges for sites at, near, or away from sites with streamflow-gaging stations. The potential extreme peak-discharge curves as related to contributing drainage area were estimated for each of the three hydrologic regions from measured extreme peaks of record at 186 sites with streamflow-gaging stations and from measured extreme peaks at 37 sites without streamflow-gaging stations in and near the Brazos River Basin. The potential extreme peak-discharge curves generally are similar for hydrologic regions 1 and 2, and the curve for region 3 consistently is below the curves for regions 1 and 2, which indicates smaller peak discharges.

Texas↗
USGS2026

Site response models based on geometric parameters for southern California sedimentary basins

Site response in sedimentary basins is influenced by complex three-dimensional (3D) features, including trapping of seismic waves, focusing of seismic energy and basin resonance. Current ground motion models (GMMs) incorporate basin effects using one-dimensional parameters like V S30 and shear wave velocity isosurface depths, which are limited in capturing lateral and 3D effects. To address these limitations, we develop seismic site response models based on novel parameters that represent multi-dimensional properties of the Los Angeles Basin (LAB) geometry and shear wave velocity. We define a basin shape for the LAB using depth to subsurface geologic interfaces associated with the oldest sedimentary deposits (depth to a particular shear wave velocity horizon, i.e., 1.5 km/s - z 1.5 ) and the depth to the crystalline basement ( z cb ) which are determined using geologic cross sections and community seismic velocity model profiles. We explore a suite of geometric descriptors computed for the LAB and southern California, from which three parameters with the greatest predictive potential are selected and evaluated using empirical ground motion residual analyses in combination with the Boore et al. GMM. The results demonstrate that the zonal heterogeneity index ( ), standard deviation of the absolute difference between z 1.5 and z cb ( ) and standard deviation of z cb ( ) each provide a reduction in site-to-site variability ( ϕ S2S ) of empirical GMMs. The reduction in ϕ S2S is period-dependent, with average decreases of 3%, 26% and 6% for , , and , respectively. Although these reductions are modest from an engineering application perspective, they are statistically significant, underscoring the inherent difficulty in fully characterising complex basin effects. Collectively, these findings indicate that the inclusion of basin-specific geometric parameters yields measurable, albeit incremental, improvements in site response prediction and establishes a framework for the progressive refinement of seismic hazard characterisation within sedimentary basins.

California↗