Geology topics

Andrew J. Michael

Publications and source records attributed to Andrew J. Michael.

11 recordsLinked to original sources

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

The U.S. Geological Survey 2025 Puerto Rico and U.S. Virgin Islands time-independent earthquake rupture forecast

We present the 2025 U.S. Geological Survey Puerto Rico and U.S. Virgin Islands (PRVI) time‐independent earthquake rupture forecast (ERF), developed for the 2025 update to the National Seismic Hazard Model (NSHM) for PRVI. The updated ERF improves upon a prior model from 2003, including an expanded fault inventory with slip‐rate estimates, updated seismicity catalogs, and refined subduction zone geometries and deformation models. It applies the fault‐system inversion methodology to solve for rates of ruptures on modeled faults, adapted from the 2023 NSHM (NSHM23) for the western United States, including the first application of the inversion to model rates on a U.S. subduction interface. Off‐fault and intraslab seismicity are constrained by observed seismicity and use updated methods developed for NSHM23. Uncertainties in model components are substantial, and the ERF represents epistemic uncertainties through a comprehensive logic tree consisting of 1.7 billion logic‐tree branches combined across all sources.

Puerto Rico, U.S. Virgin Islands↗

USGS2026

pySATSI: A Python package for computing focal mechanism stress inversions

We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault‐plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault‐plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.

Seismological Research Letters↗

USGS2025

Capturing the uncertainty of seismicity observations in earthquake rate estimates: Implications for probabilistic seismic hazard analysis and the USGS National Seismic Hazard Model

The rate of earthquakes in a region is a fundamental input to Probabilistic Seismic Hazard Analysis. We present a Monte Carlo method for computing that rate from seismicity catalogs while including a range of data and analysis uncertainties. This method is applied to regions for which the b value is assumed to be spatially invariant. Each region is broken down into epochs for which each epoch is estimated to have a uniform magnitude of completeness ( ⁠⁠ M c ). The distribution of earthquake rates for M ≥ M c is determined for each epoch by considering the Poisson likelihood of rates given the number of observed earthquakes with M ≥ M c ⁠ . We use a Monte Carlo process to include the uncertainty in b , ⁠ M c , and individual event magnitudes. The result for each epoch is the joint distribution of the Poisson rate of earthquakes with magnitudes larger than the minimum value used to calculate hazard ( ⁠⁠ M 1 ) and the Gutenberg–Richter b values, which control the extrapolation to other magnitudes. The rate for each region is either the duration‐weighted average over the epochs or, to better capture temporal variations, we also consider mixture models. The mixture models also provide an avenue to allow temporal variations in b values. To implement this joint distribution in a logic tree, we use the mean and 95% confidence branches, each of which is parameterized with an M ≥ M 1 rate and b value. We explore different ways of defining those branches, as well as non‐Gutenberg–Richter branches, and their impact on hazard estimates. The mean hazard, but not the fractiles, is robust with respect to these choices. To illustrate these new methods, we use synthetic data and catalogs from recent U.S. Geological Survey National Seismic Hazard Models for the Central and Eastern United States and for Puerto Rico and the U.S. Virgin Islands.

central and eastern United States, Puerto Rico, U.↗

USGS2025

Using gridded seismicity to forecast the long-term spatial distribution of earthquakes for the 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

Gridded (or background) seismicity models are a critical component of probabilistic seismic hazard assessments, accounting for off‐fault and smaller‐magnitude earthquakes. They are typically developed by declustering and spatially smoothing an earthquake catalog to estimate a long‐term seismicity rate that can be used to forecast future earthquakes. Here, we present new gridded seismicity models for use in the 2025 National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI). The previous PRVI NSHM was released in 2003, and our new models incorporate updates to both data and methodology. We utilize an updated earthquake catalog based on improved Puerto Rico Seismic Network data with newly characterized completeness epochs. The catalog is divided into crustal, subduction interface, and intraslab seismicity using new methods and Slab2 subduction zone geometries. To forecast the long‐term spatial distribution of earthquakes, we use an updated methodology developed for the 2023 U.S. 50‐state NSHM, considering three declustering methods and two spatial smoothing methods based on 2D Gaussian kernels. To adapt it for the complex seismotectonics of the region, we also adopt probabilistic methods to account for events with unknown depths and uncertainties in tectonic classification, and develop a new method for spatial scaling to counteract the effects of spatial variability in network coverage while maintaining the use of smaller events. Finally, we test the performance of these spatial models in forecasting the location of M w ≥ 5earthquakes in the region. Our updated methodology improves the representation of epistemic uncertainty relative to the 2003 model, and our results demonstrate the effectiveness of the new measures we have introduced to address heterogeneities in network detection and systematically evaluate forecast performance.

Puerto Rico, U.S. Virgin Islands↗

USGS2025

Subduction zone earthquake catalog separation tool: Implementation in the USGS 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

The U.S. Geological Survey (USGS) periodically releases updates to National Seismic Hazard Model (NSHM) for the United States and its territories leveraging current scientific knowledge and methodologies to guide public policy, building codes, and risk assessments regarding potential ground shaking due to earthquakes that may result in infrastructure damage. In subduction zones, there is a need to separate the earthquake catalog into tectonic regimes to create specific seismicity models for which the most appropriate ground‐motion models are then applied. Here, we describe newly developed methods and software, called CatSep, that classifies subduction zone events into three primary tectonic regimes: crustal, interface, and intraslab. This method incorporates information about the location of the earthquake relative to the subducting slab, the depth of the Mohorovičić discontinuity, and the earthquake’s moment tensor. Applying this method is a first step in the NSHM workflow for regions covering U.S. subduction zones. Results using this subduction zone earthquake catalog separation tool for the 2025 Puerto Rico and U.S. Virgin Islands NSHM earthquake catalog are presented and analyzed.

Puerto Rico, U.S. Virgin Islands↗

USGS2025

A scientific vision and roadmap for earthquake rupture forecast developments, a USGS perspective

We articulate a scientific vision and roadmap for the development of improved Earthquake Rupture Forecast models, which are one of the two main modeling components used in modern seismic hazard and risk analysis. One primary future objective is to provide fully time-dependent models that include both elastic rebound and spatiotemporal clustering nationwide, which is particularly important for shorter-term hazard and risk considerations (e.g., earthquake insurance products). We also discuss the importance and perennial challenges associated with quantifying epistemic uncertainties, including those associated with deformation-model slip rates, un-quantified sampling errors with respect to off-fault seismicity, and any spatial covariances. The need for more physics-based approaches is also emphasized, as is the benefit of adding model valuation (quantifying usefulness) to our verification and validation protocols. Given the multidisciplinary and system-level nature of this activity, modular design is critical. Future updates will also draw from best-available science by both the United States Geological Survey and the external community. The primary goal of this paper is to highlight plans that guide research and facilitate community engagement with model development, especially with respect to lowering the entry barrier for early career scientists and engineers. The paper is written so readers can focus on the sections that interest them most (see table of contents), with the Introduction and Discussion providing a stand-alone overview and summary.

Bulletin of the Seismological Society of America↗

USGS2025

Meet the people where they are: Assessing user needs for aftershock forecast products in El Salvador, Mexico and the United States

Aftershock forecasts can help communities reduce their seismic risk by conveying how many aftershocks can be expected following a large earthquake, and how the expected number of aftershocks and their corresponding ground shaking evolves over time and space. Prior work finds that graphical forecast products may communicate such information better than only text or numbers. To identify which visual products can serve multiple user groups, we held workshops with members of several professions, including emergency managers, engineers, critical infrastructure operators, public health specialists, science communicators, and more. We conducted these workshops in El Salvador, Mexico and the United States to understand which forecast products may be effective across different countries. In these workshops, users performed small-group activities to elicit the types of aftershock forecast information that would support decisions in their respective roles and how this information would optimally be displayed. Maps of shaking hazards were frequently requested across all professions and countries, even for dissimilar forecast uses. The design of these maps, and other product needs, were differentiated by profession, country and other factors. Other forecast products, including those showing forecasts about the magnitudes and time periods of future aftershocks, also served a variety of users, but for different types of decisions. We found a greater variation in user needs by profession than by country, and that user needs also vary with time, communication channel and other contextual factors. We discuss practical implications for user-centered visual communication of operational aftershock forecasts.

International Journal for Disaster Risk Reduction↗

USGS2025

Bayesian ETAS modeling for the Pacific Northwest: Uncovering effects of tectonic regimes, regional differences, and swarms on aftershock parameters

The Pacific Northwest (PNW) of North America has high seismic hazard due to numerous earthquake sources under populated areas. It hosts several tectonic regimes and subregional seismic zones that are hypothesized to have different patterns of earthquake and aftershock occurrence. It is also predisposed to earthquake swarms, which can complicate the statistical modeling of these patterns. We present the first statistical seismicity model of the PNW catalog using the epidemic‐type aftershock sequence (ETAS) framework. We develop a Bayesian inference procedure that provides a stable estimation of both ETAS parameters and their uncertainties for different sets of PNW earthquakes, even those with very sparse catalogs. The Bayesian approach allows us to investigate how parameter estimates change between the intraslab and crustal tectonic regimes, the northern and southern PNW, and when swarms are included and excluded from the catalog. We also utilize our Bayesian framework to calculate parameter estimates under different prior beliefs about PNW seismicity, as well as to propagate catalog measurement errors into ETAS parameter estimates. We discuss the implications of parameter differences across the region for aftershock forecasting for the PNW.

Pacific Northwest↗

USGS2025

Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa

From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.

American Somoa↗

USGS2024

Preliminary observations of the April 5th, 2024, Mw4.8 New Jersey earthquake

On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (>180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault.

New Jersey↗