U.S. Department of EnergyOffice of Scientific and Technical Information
EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography
Abstract
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.
- Publication Date:
- Other Number(s):
- 1214
- Research Org.:
- DOE Geothermal Data Repository; Oak Ridge National Laboratory
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
- Collaborations:
- Oak Ridge National Laboratory
- Subject:
- 15 GEOTHERMAL ENERGY; 3D; 3D seismic structure; EGS Collab; MEQ; P-wave; S-wave; SURF; catalog; deep learning; double-difference tomography; energy; geophysics; geospatial data; geothermal; interactive; interactive visualization; machine learning; microseismic catalog; microseismicity; model; modeling; processed data; seismic; seismic tomography; transfer learning; transfer-learning; velocity
- OSTI Identifier:
- 1632061
- DOI:
- https://doi.org/10.15121/1632061
Citation Formats
Chai, Chengping, Maceira, Monica, Santos-Villalobos, Hector, Schoenball, Martin, and Venkatakrishnan, Singanallur. EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography. United States: N. p., 2020.
Web. doi:10.15121/1632061.
Chai, Chengping, Maceira, Monica, Santos-Villalobos, Hector, Schoenball, Martin, & Venkatakrishnan, Singanallur. EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography. United States. doi:https://doi.org/10.15121/1632061
Chai, Chengping, Maceira, Monica, Santos-Villalobos, Hector, Schoenball, Martin, and Venkatakrishnan, Singanallur. 2020.
"EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography". United States. doi:https://doi.org/10.15121/1632061. https://www.osti.gov/servlets/purl/1632061. Pub date:Mon Apr 20 00:00:00 EDT 2020
@article{osti_1632061,
title = {EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography},
author = {Chai, Chengping and Maceira, Monica and Santos-Villalobos, Hector and Schoenball, Martin and Venkatakrishnan, Singanallur},
abstractNote = {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.},
doi = {10.15121/1632061},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {4}
}