EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained), S-wave velocity (km/s), and S-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained). The Updated_MEQ_catalog text file contains event origin time, x(m), y(m), z(m), error in x (m), error in y (m), error in z (m), and RMS misfit (millisecond). The 3D_seismic_P-wave_velocity_model animation file shows slices of the 3D P-wave velocity model. The 3D_seismic_S-wave_velocity_model animation file shows slices of the 3D S-wave velocity model. The Interactive_MEQ_locations API file is an interactive visualization of the updated microseismic event locations. The visualization allows users to view the event locations by dragging, rotating, and zooming in.

References: Chai, C., Maceira, M., Santos-Villalobos, H. J., Venkatakrishnan, S. V., Schoenball, M., and EGS Collab Team, 2020, Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab Project, in PROCEEDINGS, 45th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California, 45, 1266-1276.

Data e Risorse

Campo Valore
DOI 10.15121/1632061
accessLevel public
bureauCode {019:20}
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dataQuality true
identifier https://data.openei.org/submissions/3853
issued 2020-04-20T06:00:00Z
landingPage https://gdr.openei.org/submissions/1214
license https://creativecommons.org/licenses/by/4.0/
modified 2021-05-17T16:13:51Z
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programCode {019:006}
projectLead Lauren Boyd
projectNumber EE0032708
projectTitle EGS Collab
publisher Oak Ridge National Laboratory
resource-type Dataset
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Gruppi
  • AmeriGEOSS
  • National Provider
  • North America
Tag
  • 3d
  • 3d-seismic-structure
  • amerigeo
  • amerigeoss
  • catalog
  • ckan
  • deep-learning
  • double-difference-tomography
  • egs-collab
  • energy
  • geo
  • geophysics
  • geospatial-data
  • geoss
  • geothermal
  • interactive
  • interactive-visualization
  • machine-learning
  • meq
  • microseismic-catalog
  • microseismicity
  • model
  • modeling
  • national
  • north-america
  • p-wave
  • processed-data
  • s-wave
  • seismic
  • seismic-tomography
  • surf
  • transfer-learning
  • united-states
  • velocity
isopen True
license_id cc-by
license_title Creative Commons Attribution
license_url http://www.opendefinition.org/licenses/cc-by
maintainer Chengping Chai
maintainer_email chaic@ornl.gov
metadata_created 2025-11-22T19:43:39.698416
metadata_modified 2025-11-22T19:43:39.698421
notes This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained), S-wave velocity (km/s), and S-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained). The Updated_MEQ_catalog text file contains event origin time, x(m), y(m), z(m), error in x (m), error in y (m), error in z (m), and RMS misfit (millisecond). The 3D_seismic_P-wave_velocity_model animation file shows slices of the 3D P-wave velocity model. The 3D_seismic_S-wave_velocity_model animation file shows slices of the 3D S-wave velocity model. The Interactive_MEQ_locations API file is an interactive visualization of the updated microseismic event locations. The visualization allows users to view the event locations by dragging, rotating, and zooming in. References: Chai, C., Maceira, M., Santos-Villalobos, H. J., Venkatakrishnan, S. V., Schoenball, M., and EGS Collab Team, 2020, Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab Project, in PROCEEDINGS, 45th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California, 45, 1266-1276.
num_resources 7
num_tags 34
title EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography