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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↗

USGS2024

Anaerobic biodegradation of perfluorooctane sulfonate (PFOS) and microbial community composition in soil amended with a dechlorinating culture and chlorinated solvents

Perfluorooctane sulfonate (PFOS), one of the most frequently detected per - and polyfluoroalkyl substances (PFAS) occurring in soil, surface water, and groundwater near sites contaminated with aqueous film-forming foam (AFFF), has proven to be recalcitrant to many destructive remedies, including chemical oxidation. We investigated the potential to utilize microbially mediated reduction (bioreduction) to degrade PFOS and other PFAS through addition of a known dehalogenating culture, WBC-2, to soil obtained from an AFFF-contaminated site. A substantial decrease in total mass of PFOS (soil and water) was observed in microcosms amended with WBC-2 and chlorinated volatile organic compound (cVOC) co-contaminants — 46.4 ± 11.0 % removal of PFOS over the 45-day experiment. In contrast, perfluorooctanoate (PFOA) and 6:2 fluorotelomer sulfonate (6:2 FTS) concentrations did not decrease in the same microcosms. The low or non-detectable concentrations of potential metabolites in full PFAS analyses, including after application of the total oxidizable precursor assay, indicated that defluorination occurred to non-fluorinated compounds or ultrashort-chain PFAS. Nevertheless, additional research on the metabolites and degradation pathways is needed. Population abundances of known dehalorespirers did not change with PFOS removal during the experiment, making their association with PFOS removal unclear. An increased abundance of sulfate reducers in the genus Desulfosporosinus (Firmicutes) and Sulfurospirillum (Campilobacterota) was observed with PFOS removal, most likely linked to initiation of biodegradation by desulfonation. These results have important implications for development of in situ bioremediation methods for PFAS and advancing knowledge of natural attenuation processes.

Science of the Total Environment↗

USGS2025

Dendroseismological investigation of redwood trees along the North Coast section of the San Andreas Fault

Sequoia sempervirens (coast redwood) tree rings have the potential to annually resolve late-Holocene earthquakes on the northern San Andreas Fault based on direct (e.g., physical damage) and indirect (e.g., co-seismic environmental change) impacts, but scarcity of suitable samples and challenges crossdating this long-lived species have limited progress. More precise dating of the pre-1906 (penultimate) earthquake can improve hazard assessment and understanding of rupture segmentation. We target old trees (maximum >815 yr) along the North Coast section of the fault (increment cores via rope-climbing, 11 living trees; plunge cuts, 23 stumps) and employ complementary disturbance detection methods including radial-growth averaging (tree- and series-level), cataloging anatomical indicators (e.g., traumatic resin ducts, TRD), and dating structural components (e.g., reiterated trunks, leans). Multi-centennial ring-width chronologies at Fort Ross (1569−2023) and Gualala (1397−2023) promote continued study with incomplete crossdating limiting utilization of some series. Growth pulses (reductions, releases) and TRD dispersed across the record reflect dynamic environments that obfuscate detection of earthquake signals. The 1906 earthquake did not leave strong signatures on most trees, and when it did, within-tree response varied from normal presentation to discoloration, TRD, and missing rings. Synchrony of indicators at both locations identified 1678−1680 (6 of 15 trees) and 1698−1700 (8 of 16 trees) as the strongest disturbances among dated rings in the time range of the penultimate earthquake, peaking at 1698 (15.7 % of possible growth and anatomical indicators), but the triggering mechanisms for these events are unknown.

California↗

USGS2025

Lessons in business recovery following the 2023 Kahramanmaraş earthquake sequence, Türkiye informed by women entrepreneurs

On 6 February 2023, Southern Türkiye was hit by devastating earthquakes, directly affecting over 14 million people in 11 cities, causing more than 50,000 deaths and the destruction of more than 800,000 buildings. This article goes beyond the physical damage imposed by the catastrophe to discuss the effects of the earthquakes on the operations of women-owned businesses. The mixed-method study with entrepreneurs belonging to a women’s business association operating in a moderately disrupted part of the region explores their struggles and recovery expectations. Thirty-five questionnaires were analyzed to identify the reasons for business closure, challenges, and needs faced in the post-disaster period and their recovery strategies. In addition, 23 entrepreneurs participated in roundtable discussions to provide a broader context to their responses to survey topics as well as lessons learned. Across both the survey and roundtables, while many respondents reported minor physical damage to their building, they also experienced financial and personal challenges from disruption to equipment, infrastructure, services, supply chains, institutional decisions, employee well-being, and customer base. Many used their business resources and personal savings to assist employees and others in the community. The women entrepreneurs often felt their recovery needs were ignored by government and private relief organizations and encountered barriers to receiving assistance from public and private institutions. Organizing together as women in business, even informally, provided mutual support during the crisis and recovery periods and catalyzed their role in support of their communities. The results illuminate functional community recovery as a balance of recovery of built infrastructure functionality and recovery of the broader social and economic fabric of the community.

southern Turkey↗

USGS2026

Top-down targeted network analysis of critical mineral commodities applied to international geochemistry database

The global demand for critical mineral commodities is rapidly increasing, making domestic production an important factor in supporting the economy and national security. Large scale, publicly available geochemical databases allow for the application of data informatics methods to interrogate critical mineral commodities data for correlations in deposit formation and distribution, particularly for identifying enrichment of multiple critical mineral commodities at the same deposit. In this study, we applied network analysis to the Critical Minerals Mapping Initiative (CMMI) ore geochemistry (Critical Minerals in Ores, CMiO) database to identify the high concentration (defined as 10× bulk crustal abundance) co-occurrence of different critical mineral commodities across a mineral system hierarchy from deposit environments to individual deposits. Identifying patterns or unique outliers in enrichment in network communities will allow for the location of secondary critical mineral commodity resources from under-utilized deposits. We find trends in the enrichment of critical mineral commodities in network-communities between the elements praseodymium (Pr), neodymium (Nd), terbium (Tb), and dysprosium (Dy) across multiple CMiO database deposit environments and groups down to specific deposit types and sites. A separate trend in network community deposition is observed as well between iridium (Ir) and platinum (Pt) in deposit environments, groups, types, and sites. Network analysis focused on critical minerals in magmatic-hydrothermal deposits identified multiple deposit sites from different deposit types within the CMiO database with concentrations of Dy, Nd, Tb, Pr, Ir, and Pt that are at least ten times greater than the crustal average. This approach can be applied to any target element(s) or deposit(s) of interest, allowing broad investigation of co-enriched critical mineral commodities.

Journal of Geochemical Exploration↗

USGS2026

Making many out of one: Synthetic geologic deformation model distributions for use in USGS NSHM25‐PRVI Puerto Rico-U.S. Virgin Island update

A key use‐case of geologic slip rates is within deformation models used in probabilistic seismic hazard analyses. Field‐derived geologic slip rates have formed the cornerstone of deformation models in such applications for decades. Recent advancements in seismic hazard analyses have expanded the use of faults for which geologic slip rates are not well constrained using categorical slip rate estimates. Because of these advancements, application of a geologic deformation model for use in 2025 U.S. Geological Survey National Seismic Hazard Model Puerto Rico‐U.S. Virgin Islands (NSHM25‐PRVI) proved challenging due to: (1) a lack of field‐based geologic slip rates, and (2) a lack of epistemic uncertainty distributions within a broad range of estimated slip rates. Preliminary versions of the NSHM25‐PRVI model sampled these slip rate bins in a coincident manner along preferred and extreme value branches, which yielded untenable correlations in mean hazard results. To minimize the influence of correlated uncertainties amid these challenges, we develop a synthetic epistemic uncertainty distribution for deformation rate on each crustal fault. Each fault has a weighting schema across four possible distribution shapes: uniform, normal, triangular favoring local minima, and triangular favoring local maxima. The synthetic distributions are then sampled several times for each logic tree branch. The results provide a more realistic distribution of rates across the study region as compared with using correlated extrema sampling. This exploration of our method in a small region like PRVI can pave the way for larger‐scale, more complicated applications (e.g., western United States).

Puerto Rico, US Virgin Islands↗

USGS2025

Rainfall thresholds for postfire debris-flow initiation vary with short-duration rainfall climatology

The size, frequency, and geographic scope of severe wildfires are expanding across the globe, including in the Western United States. Recently burned steeplands have an increased likelihood of debris flows, which pose hazards to downstream communities. The conditions for postfire debris-flow initiation are commonly expressed as rainfall intensity-duration thresholds, which can be estimated given sufficient observational history. However, the spread of wildfire across diverse climates poses a challenge for accurate threshold prediction in areas with limited observations. Studies of mass-movement processes in unburned areas indicate that thresholds vary with local climate, such that higher rainfall rates are required for initiation in climates characterized by frequent intense rainfall. Here, we use three independent methods to test whether initiation of postfire runoff-generated debris flows across the Western United States varies similarly with climate. Through the compilation of observed thresholds at various fires, analysis of the spatial density of observed debris flows, and quantification of feature importance at different spatial scales, we show that postfire debris-flow initiation thresholds vary systematically with short-duration rainfall-intensity climatology. The predictive power of climatological data sets that are readily available before a fire occurs offers a much-needed tool for hazard management in regions that are facing increased wildfire activity, have sparse observational history, and/or have limited resources for field-based hazard assessment. Furthermore, if the observed variation in thresholds reflects long-term adjustment of the landscape to local climate, rapid shifts in rainfall intensity related to climate change will likely induce spatially variable shifts in postfire debris-flow likelihood.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2023

Evaluation of models for estimating hydraulic conductivity in glacial aquifers from NMR logging

Nuclear magnetic resonance (NMR) logging is a promising method for estimating hydraulic conductivity ( K ). During the past ∼60 years, NMR logging has been used for petroleum applications, and different models have been developed for deriving estimates of permeability. These models involve calibration parameters whose values were determined through decades of research on sandstones and carbonates. We assessed the use of five models to derive estimates of K in glacial aquifers from NMR logging data acquired in two wells at each of two field sites in central Wisconsin, USA. Measurements of K , obtained with a direct push permeameter (DPP), K DPP , were used to obtain the calibration parameters in the Schlumberger-Doll Research, Seevers, Timur-Coates, Kozeny-Godefroy, and sum-of-echoes (SOE) models so as to predict K from the NMR data; and were also used to assess the ability of the models to predict K DPP . We obtained four well-scale calibration parameter values for each model using the NMR and DPP measurements in each well; and one study-scale parameter value for each model by using all data. The SOE model achieved an agreement with K DPP that matched or exceeded that of the other models. The Timur-Coates estimates of K were found to be substantially different from K DPP . Although the well-scale parameter values for the Schlumberger-Doll, Seevers, and SOE models were found to vary by less than a factor of 2, more research is needed to confirm their general applicability so that site-specific calibration is not required to obtain accurate estimates of K from NMR logging data.

Wisconsin↗

USGS2024

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Alaska↗

USGS2025

A generalized deep learning model to detect and classify volcano seismicity

Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] ( 10.1029/2024JB029194 ) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network ( VOISS-Net ) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings.

Volcanica↗

USGS2026

Re-examining the growing degree day minimum for grass carp spawning initiation in Lake Erie: Have we missed the mark?

Understanding the timing of life history processes of invasive fish species, such as adult aggregations during spawning, allows natural resource managers to optimize targeted population control efforts. Temperature-based estimates of the timing of spawning initiation for grass carp ( Ctenopharyngodon idella ) exist from its native range, but have not previously been developed for grass carp reproduction in its invaded range in the Laurentian Great Lakes of North America. We accounted for differences among methods across studies and calculated cumulative growing degree days (GDD) for the dates of grass carp egg capture in Lake Erie tributaries (Sandusky, Maumee, and Huron rivers) during 2015–2024 and compared these values to the existing GDD base 15°C requirement estimate from the native range (633 GDD–15°C). The earliest egg detection in Lake Erie (Sandusky River) was 19 GDD–15°C or 361 GDD–5°C (latest egg detection was 2285 GDD–5°C from the Sandusky River), indicating that spawning was initiated up to 25 days earlier than would be predicted by the native range GDD. Moreover, spawning in Lake Erie tributaries occurred earlier than predicted for 58% of spawning events examined based on the native range GDD. The GDD values for spawning initiation in Lake Erie tributaries can be used to refine the timing of control efforts targeting pre-spawn and spawning adult fish, inform the timing of egg sampling, and provide linkages to existing early life stage models to infer the length of the growing season available for age-0 fish to reassess tributary suitability.

Lake Erie, Maumee, River, Sandusky river↗

USGS2025

Land-based nutrient flux to a fringing reef: Insights from Ofu Island, American Samoa

Submarine groundwater discharge (SGD) is a critical driver of nutrient transport in coral reef ecosystems, shaping water quality, primary productivity, and overall reef health. This study quantifies SGD fluxes and associated nutrient dynamics in two reef flat pools within the Ofu Unit of the National Park of American Samoa: Papaloloa and Fatuana. A multi-method approach integrating unoccupied aerial system-based thermal infrared (UAS-TIR) surveys, radon-based SGD measurements, multichannel electrical resistivity tomography (ERT), and discrete water sampling was used to assess SGD rates and nutrient contributions. UAS-TIR imagery revealed cooler sea surface temperatures in both pools, indicative of SGD, with the higher fluxes observed in Papaloloa. Radon measurements revealed a strong inverse correlation between SGD rates and tidal stage, with a more immediate SGD response at Papaloloa due to its highly permeable calcareous sand and gravel substrate. In contrast, a 2–3-hour lag in SGD response at Fatuana suggests discharge from a more inland aquifer that has lower diffusivity. Nutrient concentrations correlated with temperature and salinity, confirming SGD as the dominant nutrient transport mechanism, whereas isotopic analyses indicated inputs from both groundwater and potential anthropogenic sources. Despite lower SGD flux at Fatuana, higher algal cover suggests additional factors influencing algal proliferation, including substrate availability and hydrodynamic conditions. Excess nutrient inputs from SGD may contribute to algal overgrowth, which threatens Ofu’s thermally tolerant corals by increasing competition for space and light. These findings underscore the complexity of SGD-mediated nutrient dynamics in reef environments and emphasize the need for integrated hydrological and ecological assessments to support effective reef conservation and management strategies.

American Samoa, Ofu Island, Olosega Island↗

USGS2024

Simulated mean monthly groundwater-transported nitrogen loads in watersheds on the north shore of Long Island Sound, 1993–2022

Elevated nitrogen loads are pervasive in the Long Island Sound, an estuary that receives freshwater and nutrients from both surface-water and groundwater discharge. Surface-water nitrogen loads to the Long Island Sound are relatively well characterized, but less is known about groundwater-transported nitrogen loads. Prior work on the northern shore of Long Island Sound (Connecticut and areas of New York and Rhode Island) suggested that groundwater travel times are relatively short (median less than 2 years) and that decade-long nutrient legacies are not widespread. Because the travel times are short, groundwater flow and nutrient loads likely vary substantially between months. In the current study, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection, developed a set of models to better characterize spatial and temporal patterns of groundwater-transported nitrogen loading from atmospheric deposition, septic systems, and fertilizers within the study area. The models provide an estimate, with uncertainty, of groundwater-transported nitrogen loads in the study area, filling a key gap in the nitrogen budget for Long Island Sound. The models also highlight the spatial and temporal variation in nitrogen loading throughout the study area. The modeling workflow involved four models. (1) A soil-water-balance model was developed by using the Soil-Water-Balance software to simulate groundwater recharge across the study area for water years 2005 through 2022. The simulated mean monthly recharge from the soil-water-balance model was used as input into a groundwater-flow model. (2) The groundwater-flow model was developed by using the MODFLOW 6 software and data for water years 1993 through 2022 and simulates average monthly hydrologic conditions. The groundwater-flow model was calibrated by using the Iterative Ensemble Smoother method within the PEST++ software. The Iterative Ensemble Smoother method generates an ensemble of sets of parameter values, with each set producing reasonable simulated hydrologic parameter values. (3) An ensemble of MODPATH particle-tracking simulations were run to generate particle flow paths and travel times, with each simulation using a different set of the flow model parameters. (4) A nitrogen load model uses the MODPATH simulation outputs to track nitrogen from the land surface through multiple attenuation zones until it discharges into fresh or saline surface water. As with the groundwater-flow model, the nitrogen model simulated average monthly groundwater-transported nitrogen loads for water years 1993 through 2022. One novel aspect of the nitrogen load model is that the nitrogen attenuation parameters were calibrated to observed nitrogen loads. Across the ensemble of simulated nitrogen loads, the median study-area-wide monthly simulated nitrogen loads from the aquifer to Long Island Sound throughout the year ranged from 900 to 18,600 kilograms of nitrogen per day, with a median load of 5,100 kilograms of nitrogen per day. The simulated loads were based on average monthly conditions for water years 1993 through 2022. Loads were highest during the winter and early spring and lowest during the late summer. However, simulated travel times for groundwater and nitrogen loads discharged to Long Island Sound during summer were longer than travel times for groundwater and loads discharged during the winter, indicating that, on average, groundwater discharged during summer traveled along different, and longer, flow paths, than groundwater discharged during winter. This indicates that summer loads would respond more slowly to changes in nitrogen inputs at the water table than winter loads. Over the entire study area, approximately 15 percent of the simulated load is from atmospheric deposition sources, 30 to 40 percent is from fertilizer, and 50 to 60 percent is from septic systems. The final analysis of the study involved simulating the change in groundwater-transported nitrogen load in response to upgrading septic systems or reducing fertilizing inputs to areas of turf grass. Both management interventions reduced the groundwater-transported nitrogen load, and reductions were greater in areas with greater loads from septic systems or turf-grass fertilizers. The delay between management actions and substantial reductions in groundwater-transported nitrogen loads varied seasonally; loads during the late summer months remained elevated longer than the winter loads.

Connecticut, Rhode Island↗

USGS2025

Projecting stream water quality using Weighted Regression on Time, Discharge, and Season (WRTDS): An example with drought conditions in the Delaware River Basin

Future water availability depends on understanding the responses of constituent concentrations to hydrologic change. Projecting future water quality remains a methodological challenge, particularly when using discrete observations with limited temporal resolution. This study introduces Weighted Regression on Time, Discharge, and Season for Projection (WRTDS-P), a novel, computationally efficient method that enables the projection of daily stream water quality under varying hydrologic conditions using commonly available discrete monitoring data. WRTDS-P model performance was validated using 39 sites in the Delaware River Basin (DRB) and four key constituents: specific conductance (SC), nitrate (NO 3 − ), magnesium (Mg 2+ ) and calcium (Ca 2+ ). Projections were tested against holdout data from the final 1 to 5 years of each time series, demonstrating robust predictive capability, with median Nash-Sutcliffe efficiencies of 0.67 for SC, 0.56 for NO 3 − , 0.65 for Ca 2+ , and 0.79 for Mg 2+ . Model uncertainty was correlated with indicators of hydrologic or geochemical mass-sinks, such as groundwater storage and adsorption in wetland soils. Drought scenario analyses for SC used ranges of reduced discharge including flows from the 1965 drought of record. Scenarios predicted widespread increases of SC, especially in southern DRB streams where baseline SC levels are already elevated. Fractional increases of SC were more uniformly distributed, indicating potential risk to sensitive ecosystems. Notably, drought-induced SC increases were positively correlated with interannual SC trends, indicating that hydrologic extremes could exacerbate ongoing salinization. This work provides a transferable and interpretable framework for projecting future water quality and assessing hydrologic risk to water resources and aquatic ecosystems.

Delaware, New Jersey, New York, Pennsylvania↗

USGS2025

International data gaps at the Center for Engineering Strong Motion Data

The Center for Engineering Strong Motion Data (CESMD) is utilized by seismologists, engineers, and disaster management professionals in the US and has historically achieved and distributed waveforms from across the globe for significant earthquakes. The increased access to the waveforms via Web API (Application Programming Interface) offers a unique opportunity to provide the community complete datasets, sampling a variety of tectonic environments and geologic conditions, increasing the number of available ground motion records for use in ground motion models (GMMs) and improving the accuracy of earthquake engineering evaluations. The objective of this study is to programmatically identify gaps in global event data from the past decade and backfill missing data gaps at CESMD. We first compare the CESMD catalog with the Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog identifying regions and time periods where strong-motion data is limited or inadequate. To backfill datasets at CESMD for significant events, we pinpoint regions and time intervals that lack information, creating a list of events for which we’d like to obtain data. An important facet of this work is identifying the source of data and metadata across earthquake repositories around the world and integrating these data repositories into our current strong-motion data processing workflow. In parallel with these newly processed datasets, we are developing a script to produce data origination citations to include provenance and attribution information to associate with respective datasets at CESMD. We showcase our methodology for identifying and filling data gaps at CESMD using three case studies (the 2018 Anchorage Alaska earthquake sequence, seismicity associated with the 2018 Hawaiian Kilauea volcano eruption, and several earthquakes in Turkey) and then outline our strategy to apply our data gap backfilling methods on an international scale.

Conference Paper↗

USGS2026

New measurements indicate that natural geologic methane emissions from microseepage in the Michigan Basin are likely negligible

The magnitude of natural geologic methane (CH 4 ) emissions to the atmosphere (including emissions of fossil CH 4 from offshore and onshore gas and oil seeps, diffuse microseepage, mud volcanoes, volcanic vents, and geothermal areas) is highly uncertain. The largest component of geologic emissions is thought to be microseepage, which is the diffuse flux of CH 4 from soils across large areas of productive hydrocarbon basins. The accuracy of existing bottom-up estimates of microseepage is limited by low spatial coverage of published microseepage measurements. We present the first soil—atmosphere CH 4 flux measurements from Michigan Basin, USA. Results from 335 measurements taken during summer and winter seasons across a large portion of the basin suggest microseepage is nonexistent in the sampled region. Even areas with predictive features for microseepage (e.g., underlying mature, organically rich source rocks, proven gas accumulations, faults, and lineaments) yield null or negative fluxes, suggesting that CH 4 emissions from microseepage are negligible throughout our study region. A Monte Carlo method was used to place an upper limit on the regional-mean microseepage flux, in which synthetic patchy microseepage distributions were generated and tested against our observations to assess the impact of possible emission hot spots that were missed by our sampling strategy. Our analysis finds it is very unlikely that regional-mean emissions are as high as assumed in a previous global microseepage study. The observed lack of seepage may be explained by groundwater flow, active methanotrophy, glacial sediments, and bedded salt deposits, which could inhibit vertical gas migration and release to atmosphere.

Michigan↗

USGS2026

Structural evolution and slip rate variations through time of the Puente-Hills blind-thrust fault beneath Los Angeles: Implications for seismic hazard and folding kinematics

Using seismic reflection profiles, historical well logging data, and luminescence and radiocarbon ages, we determine a Pleistocene-Holocene slip history for the central, Santa Fe Springs segment of the Puente Hills blind-thrust fault (PHT), a major seismogenic fault situated beneath the urbanized Los Angeles metropolitan region. We analyze the geometry of correlative stratigraphic units in the forelimb and backlimb of the overlying growth-fold, the Santa Fe Springs anticline, and determine the uplift of seven age-correlative markers. Uplift measurements are converted to thrust displacements on the underlying PHT using a structural method laid out by Don et al. (2022, https://doi.org/10.1785/0120220048 ) that accounts for the geometry of the fault. These data indicate that deep thrust displacement on the PHT is partially consumed updip in the creation of a hanging-wall fault-bend fold, with forelimb growth strata recording <80% of the slip documented within the backlimb. Chronological data from growth strata yield age constraints for folding and faulting on the underlying PHT, providing a detailed incremental slip history derived from both the forelimb and backlimb folding for seven discrete growth horizons, spanning the past 1.4 million years. The resulting six incremental slip rates demonstrate that fault slip has varied through time from the middle Pleistocene to the Holocene. Moreover, these results reveal synchronous acceleration of both the central, Santa Fe Springs and western, Los Angeles segments of the fault system since late Pleistocene time (after 200 ka) and slip rates of greater than 2 mm/yr on the downdip, backlimb, fault-ramp below the anticline.

California↗

USGS2024

The new self-anchored suspension (SAS) San Francisco Bay Bridge- Its response to a small earthquake

This paper presents a summary of previously published work (Celebi 2023) related to the new Self-Anchored Suspension (SAS) bridge that went into service within the last decade as a replacement for the older truss bridge spanning between Yerba Buena Island and Oakland, California, within the San Francisco Bay Area. During the October 19, 1989 M6.9 Loma Prieta earthquake, which occurred ~100 km south of the Bay Bridge, a section of the upper deck of the truss bridge fell onto the lower deck – thus closing this important lifeline between San Francisco and Oakland. The SAS is unique, self-anchored, and suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the decks. The SAS bridge is extensively instrumented by the California Geological Survey’s Strong Motion Instrumentation Program (CSMIP). There are approximately 85 channels of accelerometers in the seismic monitoring system that recorded the October 14, 2019 Mw4.6 Pleasant Hill earthquake. The data allow a complex but identifiable coupled response of the deck, tower, and cable system. Both acceleration and displacement time-history data are used to extract significant frequencies using system identification methods, including spectral analyses. Results are compared to those from finite-element-model (FEM) analyses carried out during the design and analysis process of the bridge in 2002 (Nader et al. 2002). There are differences between FEM analyses results and those from the low amplitude shaking caused by a seismic event. An apparent frequency (period) of the SAS bridge is assessed (approximately 5.2 seconds). In a plot of deck length versus period, there is an almost linear relationship with periods of other regular suspension bridges, such as the Golden Gate Bridge and the Carquinez Bridge, both in the San Francisco Bay.

California↗