Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Journal of Analytical and Applied Pyrolysis”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,689 records · Page 14Linked to original sources
USGS2025

An evaluation of the effects of different deicing salt application rates on three watersheds in Essex County, New York

The U.S. Geological Survey, in cooperation with the New York State Department of Transportation, evaluated the effects of different deicing salt application rates on surface water, groundwater, and highway runoff quality near State highways in northern New York. Three reaches of State highways were tested with different deicing treatments between October 2019 and November 2022: a salt-sand mixture (Treatment A), a salt mixture applied at a lower rate (Treatment B), and a control mixture consistent with typical deicing salt amounts and application rates. Data on pavement conditions and the quality of surface water, highway runoff, and groundwater were collected. Surface electromagnetic data were also collected. Surface-water and groundwater quality downgradient from the State highways were compared with water quality at upgradient locations. The percentage of snow or ice coverage was used to evaluate the effectiveness of the salt applications. This report provides an overview of the transport of deicing salt. The Treatment B watershed had deicing mixture applied more frequently than other highway reaches, which caused it to have the highest annual total chloride application. Despite differences in chloride application, flow-weighted mean chloride concentrations in highway runoff were comparable across treatments. Chloride concentrations were elevated in surface water and groundwater downgradient from highways relative to chloride concentrations upgradient from highways. A chloride mass balance, calculated for one treatment watershed, indicated that groundwater affected by legacy deicing practices may be contributing additional chloride to surface water. Spatial patterns from electromagnetic surveys show a shallow saline plume alongside the highway in that area. Differences in winter severity and pavement-surface conditions drove deicing salt applications in the treatment areas. This study found that several factors affect chloride loads in the watersheds, including variable winter conditions, adaptive snow and ice management, legacy management practices, and area-specific aquifer and groundwater conditions.

New York↗
USGS2024

Insights on gas hydrate formation and growth within an interbedded sand reservoir from well logging at the Qiongdongnan Basin, South China Sea

Although variable well log resolution and its control on saturation estimation has been studied, it has not been directly applied to a specific location to explore the nature of gas hydrate within a sand reservoir. We applied in-situ measurements of resistivities, neutron porosity, and gamma ray at two sites in the Qiongdongnan Basin, South China Sea (QDN-W05–2021 and QDN-W08–2021) to investigate the reservoir parameters of a hydrate-bearing sand reservoir. Our results show that gas hydrate is distributed in 5 zones with a total thickness of 10.7 m and an average saturation of 69% at the QDN-W05–2021 site, while they are distributed in 2 zones with a total thickness of 4.3 m and an average saturation of 49% at the QDN-W08–2021 site. We found that variances in saturations estimated from lateral-extra deep button (RX), phase shift (P40H-P40L), and attenuation (A40H-A40L) resistivities within the laterally mapped continuous sand body were affected by the nature of gas hydrate occurrences. Results indicate gas hydrate forms and accumulates at the center of the sand layer and tends to be less or not present toward the top and base. Integrated with seismic data, the in-situ measurements provide insights in the evolution of a mushroom-shaped, hydrate-gas reservoir system. In the system, free gas is likely horizontally transported from the top-center of the gas chimney to the surrounding areas in the early stage dominated by a warm-gas environment, whereas hydrate forms in the opposite pathway starting from the surrounding areas in the following stage with temperature reducing. Our study suggests that high-resolution in-situ measurements not only are a tool to identify the physical properties, but also can be used to help explain the physical process of hydrate growth and accumulation.

Marine Geology↗
USGS2025

An empirical Green’s function approach for isolating directivity effects in earthquake ground-motion amplitudes

In this study, we apply an empirical Green’s function (eGf) method within a ground‐motion modeling framework to mitigate trade‐offs between source, path, and site effects. Many physical processes contribute to spatial variations in observed ground motions, including earthquake radiation pattern, directivity, variable path attenuation, and site effects. Current nonergodic ground‐motion models use spatially varying coefficients for path and site effects, but they do not address trade‐offs with complex earthquake source effects. To quantify the influence of directivity on ground‐motion amplitudes, we use records from multiple smaller earthquakes with epicenters near that of a larger event. We use these small magnitude events as eGfs and estimate repeatable path and site effects at individual stations, assuming that the average adjustments are not controlled by directivity. We adjust residuals from the larger earthquake using the eGf terms, isolating effects related to the rupture. This method clearly enhances the observed broadband directivity observed in the 2022 M 5.1 and 2007 M 5.4 Alum Rock earthquake ground motions, reinforcing the conclusion that their ruptures were unilateral. For the 2004 M 6.0 Parkfield earthquake, we find a bilateral rupture model better fits the data because variations in rupture velocity, slip rate, and slip distribution seem to have a stronger effect on the ground motions than rupture direction alone. Applying eGf adjustments reduces the standard deviation of the rupture models over the three earthquakes by 32% on average and by up to 57% for the 2022 Alum Rock earthquake, confirming we have effectively removed repeatable effects related to the wave propagation path and site response. We propose a novel measure of the frequency‐dependent directivity amplification strength as the reduction in ground‐motion residual variability gained by fitting a directivity model; for the three earthquakes considered, this parameter varies between 25% and 75%, indicating that directivity can strongly influence ground motions and should be considered in ground‐motion modeling.

California↗
USGS2024

SCEC/USGS Community Stress Drop Validation Study: How spectral fitting approaches influence measured source parameters

Spectral source parameters used to estimate an earthquake’s stress drop (Δσ) can vary significantly across measurement approaches. The Statewide California Earthquake Center/U.S. Geological Survey Community Stress-Drop Validation Study was initiated to compare source parameter estimates, focusing initially on a dataset from the 2019 Ridgecrest earthquake sequence. As part of that validation effort, here we focus on one potential source of uncertainty: whether spectral fitting approaches alone, applied to a common set of spectra from the 2019 Ridgecrest sequence result in different source parameter estimates. By using a common set of benchmark spectra analyzed across a consistent frequency band of 1–40 Hz, we eliminate many sources of variability. A subgroup of validation study participants volunteered to estimate the low-frequency displacement (Ω0) and corner frequency ( f c ) by fitting a smooth function to benchmark displacement spectra. Participants used linear- or log-sampled spectra, assumed a Brune or Boatwright spectral model, and applied different misfit criteria. We compare 17 approaches used to estimate Ω0, f c , and Δσ for 54 earthquake spectra. Our results reveal that 35% of events have Δσ estimates within a factor of two, whereas others exhibit variations exceeding an order of magnitude. The variability in Ω0 and f c can largely be attributed to whether a spectrum is consistent with the smooth function of an idealized simple crack model. The trade-off between Ω0 and f c may be more pronounced when using linearly sampled spectra, as higher frequency spectral bumps control the fits. As expected, methods that assumed a Boatwright model tended to have lower Ω0 and somewhat higher f c compared to those assuming a Brune model, although resulting Δσ estimates are similar. When compared to the overall validation study results, the fitting approach alone may account for between 5% and 90% (25% on average) of the total variability in spectral Δσ.

California↗
USGS2026

Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations

Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗
USGS2026

Aspergillosis (Avian) case definition for wildlife

Diagnostic laboratories receive carcasses and samples for diagnostic evaluation and pathogen/toxin detection. Case definitions bring clarity and consistency to the evaluation process. Their use within and between organizations allows more uniform reporting of diseases and etiologic agents. The intent of a case definition is to provide scientifically based criteria for determining: (a) if an individual carcass has a specific disease and degree of confidence in that diagnosis and (b) if there is evidence of a pathogen or toxin in a carcass or sample (for example, swab, tissue sample, skin scraping, blood/serum sample, environmental sample, or other). This case definition is specific to aspergillosis and applies to all avian species.

Techniques and Methods↗
USGS2024

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California↗
USGS2025

Grammar to graph—An approach for semantic transformation of annotations to triples

Data annotation is the process of labeling data to show the outcome that a related data model should predict. In this study, annotation data were transformed into semantic graph triples, mainly for use with the Resource Description Framework (RDF), a type of entity-relationship-attribute data model for graph databases. The transformation of annotation data to semantic graph triples provides complex linguistic meaning with data handling advantages such as reduced data storage needs, improved logical specification of relations between objects, and reusable classes and properties that support logic and inference. A grammar-based framework in graph form supports user questions and queries. The words defining approximately 334 topographic feature types compiled by the U.S. Geological Survey were tokenized as units of analysis and grouped by part of speech. Their dependency relations were identified for this study using natural language processing libraries. Dependency concepts are used as structured semantic relations among part-of-speech classes. Tokens, units equivalent to words, form instances of classes and were quantified within a tabular output format using PostgreSQL data storage software. Table data were logically aligned as triples following a mapping file and stored with an ontology file using Ontop virtual triplestore software. A grammar ontology schema for the data was synchronized to match queries whose results validated the graph’s structure. The text analysis produced 8 part-of-speech classes of content words for object representations and 4 classes of function words for operational applications. Dependency relations formed 27 ontology properties for topographic subgraph structures. Token occurrences shaped overall ontology salience and formed a lexicon of syntactic terms for subgraph objects and properties. The schema ontology of class and property population shapes formed the lexicon of English terms. SPARQL Protocol and RDF Query Language (SPARQL) was used with the lexicon to conform data to RDF guidelines. This study confirms the hypothesis that although linguistic logic varies from description logic, its approximation applies to ontology design. Property and query use case patterns extracted from the analysis support queries concerning complex topographic relations and patterns normally embedded within text definitions. The method used in this study could be applied to text forms in other domains, such as survey notes.

Scientific Investigations Report↗
USGS2025

Ages and trace element fertility of porphyry-related mineralization in the Philipsburg polymetallic district, Montana, with a comparison to Butte

The Philipsburg mining district is a Mo-Cu porphyry system with associated Cordilleran polymetallic veins. Geochronology was employed to date the porphyry (~66 Ma, U/Pb in zircon) and molybdenite mineralization from the veins (~76 Ma, Re-Os). Age results suggest that the two-mineralization events model proposed by Lund et al. (2018) for the Butte district can be applied to the Philipsburg deposit. Furthermore, common fertility indicators from zircon trace elements were analyzed with variable success to characterize the existing Mo-Cu mineralization of the Philipsburg porphyry. This raises concerns about the benefits of this method and/or the importance of sampling, which could impact exploration for similar porphyry deposits.

Montana↗
USGS2025

Scientific opportunities in the National Landscape Conservation System

The National Landscape Conservation System consists of unique and beautiful places across America’s landscapes where identified resources and values are protected and science is highlighted. The mission of the National Landscape Conservation System (NLCS), which is managed by the Bureau of Land Management and is often referred to as the agency’s National Conservation Lands, is to conserve, protect, and restore nationally significant landscapes for their cultural, ecological, and scientific values. This clear inclusion of science in the NLCS mission sets the stage for individual units to serve as places of learning, teaching, discovery, and innovation. Science is an integral part of managing the National Conservation Lands, and science conducted within and across the more than 900 units that make up the NLCS can inform and influence conservation and public land management well beyond its boundaries. Here, we highlight seven core aspects of National Conservation Lands that present valuable science opportunities: (1) the scientific values for which individual units are designated; (2) the many other resources, objects, and values within units; (3) the value of units as “control” sites for understanding the effects of activities such as mineral extraction that commonly occur elsewhere on multiple-use public lands but are often prohibited within National Conservation Lands; (4) the value of units for studying the effects of activities such as recreation that regularly occur and may be intensified on National Conservation Lands; (5) the high visibility of units, which draws strong interest and engagement from scientists, partners, and the public; (6) the functioning of the units as a network managed for a common purpose, which provides an opportunity to explore cross-cutting science questions across widely varying contexts and geographies; and (7) the opportunities units provide to promote and apply Indigenous Knowledge to scientific research to manage natural and cultural resources. Because of all of these characteristics, National Conservation Lands can serve as hubs for basic and applied science that can inform management of all public lands and resources into the future. We highlight these science opportunities through examples from existing units and suggest two actions that could help further science activities and impact on National Conservation Lands.

Parks Stewardship Forum↗
USGS2026

2025 USGS National Seismic Hazard Model for Puerto Rico and the U.S. Virgin Islands: Overview of model and hazard results

The U.S. Geological Survey recently updated the National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI). The first version of the PRVI NSHM was released in 2003, and therefore this 2025 update includes over 20 years of new geologic, geophysical, and engineering data, methods, and models. Updates follow similar efforts performed in the recent 2023 50-state NSHM. However, this is the first NSHM in which we: (1) apply an inversion methodology to subduction interface fault sources in the earthquake rupture forecast (ERF) model; (2) develop scaled backbone median ground-motion models and independent aleatory variability models that are applied in the ground-motion characterization (GMC) model; and (3) calculate epistemic uncertainty related to alternative scenarios in the ERF and GMC models for all grid points in the study region. Long-term time-independent mean hazard calculations were performed for peak ground acceleration and 5%-damped pseudospectral acceleration at 21 spectral periods from 0.01- to 10.0-s, for eight National Earthquake Hazards Reduction Program site conditions ranging from V S30 = 150 to 1500 m/s, and for 2%, 5%, and 10% in 50-year probabilities of exceedance (return periods of 2475, 975, and 475 years, respectively). Epistemic uncertainty, in the form of selected percentiles, is also provided for a suite of test sites and all grid points in the study region for limited periods, site conditions, and probabilities of exceedance. Selected results, including comparisons with the 2003 PRVI NSHM, are shown and discussed for selected periods, site conditions, and probabilities of exceedance. When comparing the 2025 PRVI NSHM with the 2003 PRVI NSHM, hazard is generally higher at shorter periods and lower at longer periods, as a result of updates in both ERF and GMC models. The 2025 PRVI NSHM is applicable for return periods greater than ∼475 or less than ∼10,000 years.

Puerto Rico, U.S. Virgin Islands↗
USGS2026

Estimating aftershock risk for entry into earthquake-damaged buildings

We present a simple method to estimate the risk of experiencing strong shaking from aftershocks during entry into earthquake-damaged buildings. We compute wait times until the probability of strong ground shaking from aftershocks reaches a predefined risk threshold; for example, a 0.4 percent probability of experiencing Modified Mercalli Intensity 7 or greater shaking during the planned building entry. We also develop a relation between aftershock probability and the probability of strong shaking, so that users can reference the U.S. Geological Survey aftershock forecast during an ongoing aftershock sequence to determine if the risk threshold has been met. We apply our method to active continental regions (for example, the Western United States), stable continental regions (for example, the Central and Eastern United States), and subduction zones (for example, Cascadia or Alaska).

Open-File Report↗
USGS2024

GRAPES: Earthquake early warning by passing seismic vectors through the grapevine

Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield-based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

Shimane/HiroshimaPrefectures↗
USGS2000

Estimating the probability of elevated nitrate (NO2+NO3-N) concentrations in ground water in the Columbia Basin Ground Water Management Area, Washington

Logistic regression was used to relate anthropogenic (man-made) and natural factors to the occurrence of elevated concentrations of nitrite plus nitrate as nitrogen in ground water in the Columbia Basin Ground Water Management Area, eastern Washington. Variables that were analyzed included well depth, depth of well casing, ground-water recharge rates, presence of canals, fertilizer application amounts, soils, surficial geology, and land-use types. The variables that best explain the occurrence of nitrate concentrations above 3 milligrams per liter in wells were the amount of fertilizer applied annually within a 2-kilometer radius of a well and the depth of the well casing; the variables that best explain the occurrence of nitrate above 10 milligrams per liter included the amount of fertilizer applied annually within a 3-kilometer radius of a well, the depth of the well casing, and the mean soil hydrologic group, which is a measure of soil infiltration rate. Based on the relations between these variables and elevated nitrate concentrations, models were developed using logistic regression that predict the probability that ground water will exceed a nitrate concentration of either 3 milligrams per liter or 10 milligrams per liter. Maps were produced that illustrate the predicted probability that ground-water nitrate concentrations will exceed 3 milligrams per liter or 10 milligrams per liter for wells cased to 78 feet below land surface (median casing depth) and the predicted depth to which wells would need to be cased in order to have an 80-percent probability of drawing water with a nitrate concentration below either 3 milligrams per liter or 10 milligrams per liter. Maps showing the predicted probability for the occurrence of elevated nitrate concentrations indicate that the irrigated agricultural regions are most at risk. The predicted depths to which wells need to be cased in order to have an 80-percent chance of obtaining low nitrate ground water exceed 600 feet in the irrigated agricultural regions, whereas wells in dryland agricultural areas generally need a casing in excess of 400 feet. The predicted depth to which wells need to be cased to have at least an 80-percent chance to draw water with a nitrate concentration less than 10 milligrams per liter generally did not exceed 800 feet, with a 200-foot casing depth typical of the majority of the area.

Washington↗
USGS2025

Memory and jamming in fault zone sediments

Many subsurface processes involve transitions in granular material states, from arrested to creeping to flowing. Experiments and frameworks for idealized systems reveal that granular fabrics develop during shearing, co-evolve with applied stress, and govern such transitions. We use microtomography to test whether fabrics at two San Andreas fault sites reflect slip history and whether idealized frameworks extend to nature. Near-surface sediments within the fault zone transition between deformation patterns over the seismic cycle, including bulk/localized grain re-arrangements, individual grain fracturing, and localized zones of fracturing. Aseismic and co-seismic shearing produce distinct preferred grain orientations. Co-seismic fabrics can be preserved after centuries of aseismic strain, aseismic fabrics may be overprinted, and grain size and coordination number influence the fabrics. Idealized frameworks, namely anisotropic critical state theory, frictional jamming, and material memory, can explain our observations, and fault zone sediments likely undergo cycles of memory creation and erasure that influence rigidity spatiotemporally.

California↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗
USGS2024

The impact of source time function complexity on stress drop estimates

Earthquake stress drop—a key parameter for describing the energetics of earthquake rupture—can be estimated in several different, but theoretically equivalent, ways. However, independent estimates for the same earthquakes sometimes differ significantly. We find that earthquake source complexity plays a significant role in why theoretically (for simple rupture models) equivalent methods produce different estimates. We apply time‐ and frequency‐domain methods to estimate stress drops for real earthquakes in the SCARDEC (Seismic source ChAracteristics Retrieved from DEConvolving teleseismic body waves, Vallée and Douet, 2016 ) source time function (STF) database and analyze how rupture complexity drives stress‐drop estimate discrepancies. Specifically, we identify two complexity metrics—Brune relative energy (BRE) and spectral decay—that parameterize an earthquake’s complexity relative to the standard Brune model and strongly correlate with the estimate discrepancies. We find that the observed systematic magnitude–stress‐drop trends may reflect underlying changes in STF complexity, not necessarily trends in actual stress drop. Both the decay and BRE parameters vary systematically with magnitude, but whether this magnitude–complexity relationship is real remains unresolved.

Bulletin of the Seismological Society of America↗
USGS2024

Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance

The Uniform California Earthquake Rupture Forecast version 3-Time Dependent depicts California’s seismic faults and their activity. Its logic tree has 5760 leaves. Considering 30 more model combinations related to ground motion produces 172,800 distinct models representing so-called epistemic uncertainties. To calculate risk to a portfolio of buildings, one also considers millions of earthquakes and spatially correlated ground-motion variability. We offer a tree-trimming technique that retains the probability distribution of portfolio loss and identifies the leading sources of uncertainty for further study. We applied it to a California statewide building portfolio and various levels of nonexceedance probability between one in 100 and one in 2500. We trimmed the logic tree from 172,800 leaves to as few as 15. The result: a supercomputer that would otherwise run 24 h to estimate the distribution of one-in-250-year loss can calculate it in moments with the reduced-order model. Others can use the reduced-order model to calculate risk to different California portfolios, and scientists can prioritize study to reduce the remaining epistemic uncertainty.

Earthquake Spectra↗