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

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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.
Export Citation to RIS
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_3853, 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 May 17, 2021

Submitted Apr 20, 2020

Organization

Oak Ridge National Laboratory

Contact

Chengping Chai

865.241.1971

Authors

Chengping Chai

Oak Ridge National Laboratory

Monica Maceira

Oak Ridge National Laboratory

Hector Santos-Villalobos

Oak Ridge National Laboratory

Martin Schoenball

Lawrence Berkeley National Laboratory

Singanallur Venkatakrishnan

Oak Ridge National Laboratory

Research Areas

DOE Project Details

Project Name EGS Collab

Project Lead Lauren Boyd

Project Number EE0032708

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