Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas

The El Paso (TX)–Ciudad Juárez (MX) metropolitan area, located within the tectonically active Rio Grande Rift, has historically recorded shaking from nearby high-magnitude earthquakes (e.g., the 1887 Mw 7.6 in Sonora, MX, and the 1931 Mw 5.8 in Valentine, Texas). Application of the machine-learning...

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Main Authors: Victor Salles, Guo-Chin Dino Huang, Katerine Vallejo, Peter Eichhubl, Shuvajit Bhattacharya, Alexandros Savvaidis
Format: Article
Language:English
Published: Seismological Society of America 2025-05-01
Series:The Seismic Record
Online Access:https://doi.org/10.1785/0320250006
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author Victor Salles
Guo-Chin Dino Huang
Katerine Vallejo
Peter Eichhubl
Shuvajit Bhattacharya
Alexandros Savvaidis
author_facet Victor Salles
Guo-Chin Dino Huang
Katerine Vallejo
Peter Eichhubl
Shuvajit Bhattacharya
Alexandros Savvaidis
author_sort Victor Salles
collection DOAJ
description The El Paso (TX)–Ciudad Juárez (MX) metropolitan area, located within the tectonically active Rio Grande Rift, has historically recorded shaking from nearby high-magnitude earthquakes (e.g., the 1887 Mw 7.6 in Sonora, MX, and the 1931 Mw 5.8 in Valentine, Texas). Application of the machine-learning (ML)-based EarthQuake Compact Convolutional Transformer (EQCCT) algorithm to seismic data from 2008 to 2011 resulted in the detection of 645 seismic events in the area, lowering the magnitude detection threshold compared to public catalogs (e.g., U.S. Geological Survey ComCat with 35 events in the same period). Manual review and relocation using NonLinLoc and hierarchical clustering with GrowClust3D revealed seven seismic clusters: four clusters align with mapped Quaternary faults, whereas three clusters correspond to previously unrecognized seismogenic structures. These results demonstrate that advanced ML techniques can enhance earthquake detection and refine the understanding of regional seismicity. With new geothermal projects on the horizon in West Texas, the enhanced seismic catalog provides a more robust basis for assessing seismic hazard potential, which is critical for guiding safe geothermal development in the region.
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spelling doaj-art-e17796a4a05544f9adba6c86729aa81c2025-08-20T03:22:04ZengSeismological Society of AmericaThe Seismic Record2694-40062025-05-015218519410.1785/032025000625006Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West TexasVictor Salles0https://orcid.org/0009-0003-0328-6200Guo-Chin Dino Huang1https://orcid.org/0000-0002-2563-737XKaterine Vallejo2https://orcid.org/0000-0002-4498-8749Peter Eichhubl3https://orcid.org/0000-0002-2729-776XShuvajit Bhattacharya4https://orcid.org/0000-0002-9780-7753Alexandros Savvaidis5https://orcid.org/0000-0001-6373-5256Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, U.S.A.Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, U.S.A.Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, U.S.A.Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, U.S.A.Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, U.S.A.Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, U.S.A.The El Paso (TX)–Ciudad Juárez (MX) metropolitan area, located within the tectonically active Rio Grande Rift, has historically recorded shaking from nearby high-magnitude earthquakes (e.g., the 1887 Mw 7.6 in Sonora, MX, and the 1931 Mw 5.8 in Valentine, Texas). Application of the machine-learning (ML)-based EarthQuake Compact Convolutional Transformer (EQCCT) algorithm to seismic data from 2008 to 2011 resulted in the detection of 645 seismic events in the area, lowering the magnitude detection threshold compared to public catalogs (e.g., U.S. Geological Survey ComCat with 35 events in the same period). Manual review and relocation using NonLinLoc and hierarchical clustering with GrowClust3D revealed seven seismic clusters: four clusters align with mapped Quaternary faults, whereas three clusters correspond to previously unrecognized seismogenic structures. These results demonstrate that advanced ML techniques can enhance earthquake detection and refine the understanding of regional seismicity. With new geothermal projects on the horizon in West Texas, the enhanced seismic catalog provides a more robust basis for assessing seismic hazard potential, which is critical for guiding safe geothermal development in the region.https://doi.org/10.1785/0320250006
spellingShingle Victor Salles
Guo-Chin Dino Huang
Katerine Vallejo
Peter Eichhubl
Shuvajit Bhattacharya
Alexandros Savvaidis
Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas
The Seismic Record
title Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas
title_full Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas
title_fullStr Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas
title_full_unstemmed Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas
title_short Assessment of Seismic Hazard Potential for a Geothermal Field: A Case Study in West Texas
title_sort assessment of seismic hazard potential for a geothermal field a case study in west texas
url https://doi.org/10.1785/0320250006
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