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  5. Subduction Zone Earthquake Catalog Separation Tool: Implementation in the USGS 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

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Article
en
2025

Subduction Zone Earthquake Catalog Separation Tool: Implementation in the USGS 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

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en
2025
DOI: 10.1785/0220250078

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Eric M Thompson
Eric M Thompson

Usgs, Golden

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Kirstie L. Haynie
Eric M Thompson
Michael Hearne
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Abstract

Abstract 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.

How to cite this publication

Kirstie L. Haynie, Eric M Thompson, Michael Hearne, G. P. Hayes, D. R. Shelly, Allison M. Shumway, A. L. Llenos, Peter M. Powers (2025). Subduction Zone Earthquake Catalog Separation Tool: Implementation in the USGS 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model. , DOI: https://doi.org/10.1785/0220250078.

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Publication Details

Type

Article

Year

2025

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1785/0220250078

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