USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project, with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevada. The geophysics data include map surfaces related to gravity and magnetic data, and line and point data derived from those surfaces. Heat flow data include an interpolated map of heat flow in mW/m^2, an error surface, and well data used to construct them. The dilation and slip tendency information exist as attributes assigned to each line segment of mapped faults and geophysical lineaments.
GDR submission contains link to official USGS data release. Additional metadata available on source DOI page.
Citation Formats
TY - DATA
AB - This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project, with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevada. The geophysics data include map surfaces related to gravity and magnetic data, and line and point data derived from those surfaces. Heat flow data include an interpolated map of heat flow in mW/m^2, an error surface, and well data used to construct them. The dilation and slip tendency information exist as attributes assigned to each line segment of mapped faults and geophysical lineaments.
GDR submission contains link to official USGS data release. Additional metadata available on source DOI page.
AU - DeAngelo, Jacob
A2 - Glen, Jonathan
A3 - Siler, Drew
A4 - Faulds, James
A5 - Coolbaugh, Mark
A6 - Earney, Tait
A7 - Dean, Branden
A8 - Zielinski, Laurie
A9 - Ritzinger, Brent
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - geophisics
KW - Nevada
KW - Slip
KW - Dilation
KW - Heat Flow
KW - gravity
KW - magnetics
KW - faults
KW - geotiffs
KW - machine learning
KW - exploration
KW - characterization
KW - hydrothermal
KW - great basin
KW - geophysics
KW - pfa
LA - English
DA - 2021/06/01
PY - 2021
PB - Nevada Bureau of Mines and Geology
T1 - USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
UR - https://gdr.openei.org/submissions/1349
ER -
DeAngelo, Jacob, et al. USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada . Nevada Bureau of Mines and Geology, 1 June, 2021, Geothermal Data Repository. https://gdr.openei.org/submissions/1349.
DeAngelo, J., Glen, J., Siler, D., Faulds, J., Coolbaugh, M., Earney, T., Dean, B., Zielinski, L., & Ritzinger, B. (2021). USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada . [Data set]. Geothermal Data Repository. Nevada Bureau of Mines and Geology. https://gdr.openei.org/submissions/1349
DeAngelo, Jacob, Jonathan Glen, Drew Siler, James Faulds, Mark Coolbaugh, Tait Earney, Branden Dean, Laurie Zielinski, and Brent Ritzinger. USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada . Nevada Bureau of Mines and Geology, June, 1, 2021. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1349
@misc{GDR_Dataset_1349,
title = {USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada },
author = {DeAngelo, Jacob and Glen, Jonathan and Siler, Drew and Faulds, James and Coolbaugh, Mark and Earney, Tait and Dean, Branden and Zielinski, Laurie and Ritzinger, Brent},
abstractNote = {This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project, with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevada. The geophysics data include map surfaces related to gravity and magnetic data, and line and point data derived from those surfaces. Heat flow data include an interpolated map of heat flow in mW/m^2, an error surface, and well data used to construct them. The dilation and slip tendency information exist as attributes assigned to each line segment of mapped faults and geophysical lineaments.
GDR submission contains link to official USGS data release. Additional metadata available on source DOI page.},
url = {https://gdr.openei.org/submissions/1349},
year = {2021},
howpublished = {Geothermal Data Repository, Nevada Bureau of Mines and Geology, https://gdr.openei.org/submissions/1349},
note = {Accessed: 2025-04-25}
}
Details
Data from Jun 1, 2021
Last updated Oct 7, 2022
Submitted Aug 22, 2022
Organization
Nevada Bureau of Mines and Geology
Contact
James Faulds
775.682.8751
Authors
Keywords
geothermal, energy, geophisics, Nevada, Slip, Dilation, Heat Flow, gravity, magnetics, faults, geotiffs, machine learning, exploration, characterization, hydrothermal, great basin, geophysics, pfaDOE Project Details
Project Name Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
Project Lead Mike Weathers
Project Number EE0008762
