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Edward H. Field

Publications and source records attributed to Edward H. Field.

6 recordsLinked to original sources

USGS2026

Testing characteristic magnitude distributions in modern PSHA models

The characteristic magnitude distribution hypothesis predicts a higher rate of large earthquakes than a Gutenberg–Richter extrapolation of the small‐earthquake rate would imply. Characteristic magnitude distributions have been commonly applied to faults in probabilistic seismic hazard analysis (PSHA), and in modern models they can emerge from the way short‐term seismicity constraints are combined with long‐term geologic and geodetic constraints. We test the characteristic magnitude distribution hypothesis by comparing the fault‐based magnitude distributions from the 2023 update to the National Seismic Hazard Model (NSHM23) in the Western United States with observed seismicity over the past 93 yr. We find that observed magnitude distributions fall outside the model‐predicted confidence bounds in regions where NSHM23 produces characteristic magnitude distributions: in these regions, the model predicts higher rates of large earthquakes than are observed. An analysis of the earlier California model (Uniform California Earthquake Rupture Forecast, version 3) also reveals discrepancies between the modeled and observed magnitude distributions. In addition, we find that observed magnitude distributions near modeled faults are not significantly different from those in background regions. These results challenge the prevalence of characteristic magnitude distributions in fault‐based seismic hazard models and call for a reassessment of how disparate data sets are integrated in PSHA.

western United States↗

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

An entropic explanation for Gutenberg-Richter scaling

We develop a simple explanation for Gutenberg-Richter (G-R) size scaling of earthquakes on a single fault. We discretize the fault and consider all possible contiguous ruptures at that level of discretization. In this static model, we assume that slip scales with rupture length, and that the rupture rates at each point along the fault are consistent with an a priori long-term slip rate. These simple assumptions define an (under-determined) non-negative least-squares inverse problem. Each solution to this inverse problem is a set of earthquake rates that matches the slip-rate constraint. We use a Markov Chain Monte Carlo (MCMC) algorithm to uniformly sample the solution space assuming constant slip rates along the fault. At finer discretizations, deviations from G-R behavior decrease, which is consistent with an entropic pressure towards G-R solutions. When the fault is discretized into 10 or more segments, random solutions found by the MCMC algorithm have G-R size scaling, even though there are trivial solutions that, for example, have earthquakes of only one size. This is because there are simply far more solutions that have G-R scaling; as the problem size increases, the strong degeneracy of GR solutions results in other solutions becoming improbably rare. Also, the entropically favored G-R distribution has a b -value of approximately 1, which agrees with measured b -values in real earthquake catalogs.

JGR Solid Earth↗

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

Risk implications of Poisson assumptions and declustering inferred from a fully time-dependent earthquake forecast

We use the Third Uniform California Earthquake Rupture Forecast Epidemic Type Aftershock Sequence model, which is fully time-dependent in terms of including spatiotemporal clustering, to evaluate the effects of the Poisson assumption and declustering algorithms on statewide loss exceedance curves. The model is simulation based, meaning it produces synthetic catalogs that exhibit realistic behavior with respect to aftershocks and multi-fault earthquakes. A Poisson version of the model was constructed by randomizing event times, and the influence of two declustering algorithms was examined as well. We demonstrate that the probability of one-or-more loss exceedances (occurrence exceedance probability) is greater for the Poisson model because it has fewer seismically quiet time windows. The discrepancy between dollar loss estimates with a given exceedance probability is up to a factor of 32% but varies depending on the loss threshold (the x-axis value) and the forecast duration (we examined a range between 24 h and 50 years, with the discrepancy for the latter being negligible). We discuss how the one-or-more loss exceedance metric is questionable because it ignores all but the maximum loss experienced in each timeframe. An alternative metric based on total aggregate loss in each time window (aggregate exceedance probability) was therefore also examined, for which the Poisson model again implies higher risk at intermediate losses but lower risk at higher losses (because large, triggered events now contribute to total aggregate losses for the fully time-dependent model). We also argue that declustering is not a scientifically justifiable way to deal with full time dependence, in agreement with a chorus from other recent studies. It is difficult to draw generally applicable conclusions from our study, in part because application specific details will likely be important, but our results highlight how full time dependence can be reckoned with once authoritative forecast models are made available.

California↗