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.
Citation Formats
Oak Ridge National Laboratory. (2020). EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography [data set]. Retrieved from https://dx.doi.org/10.15121/1632061.
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., 20 Apr, 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. https://dx.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. https://dx.doi.org/10.15121/1632061. https://gdr.openei.org/submissions/1214.
@div{oedi_1214, title = {EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography}, author = {Chai, Chengping, Maceira, Monica, Santos-Villalobos, Hector, 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}, url = {https://gdr.openei.org/submissions/1214}, journal = {}, number = , volume = , place = {United States}, year = {2020}, month = {04}}
https://dx.doi.org/10.15121/1632061
Details
Data from Apr 20, 2020
Last updated Jul 18, 2024
Submitted Apr 20, 2020
Organization
Oak Ridge National Laboratory
Contact
Chengping Chai
865.241.1971
Authors
Keywords
geothermal, energy, EGS Collab, 3D seismic structure, transfer learning, deep learning, machine learning, interactive, SURF, P-wave, S-wave, microseismic catalog, seismic tomography, interactive visualization, geophysics, modeling, velocity, model, microseismicity, catalog, transfer-learning, double-difference tomography, 3D, seismic, MEQ, processed data, geospatial dataDOE Project Details
Project Name EGS Collab
Project Lead Lauren Boyd
Project Number EE0032708