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
Nevada Bureau of Mines and Geology. (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]. Retrieved from https://gdr.openei.org/submissions/1349.
DeAngelo, Jacob, Glen, Jonathan, Siler, Drew, Faulds, James, Coolbaugh, Mark, Earney, Tait, Dean, Branden, Zielinski, Laurie, and Ritzinger, Brent. 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 . United States: N.p., 01 Jun, 2021. Web. https://gdr.openei.org/submissions/1349.
DeAngelo, Jacob, Glen, Jonathan, Siler, Drew, Faulds, James, Coolbaugh, Mark, Earney, Tait, Dean, Branden, Zielinski, Laurie, & Ritzinger, Brent. 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 . United States. https://gdr.openei.org/submissions/1349
DeAngelo, Jacob, Glen, Jonathan, Siler, Drew, Faulds, James, Coolbaugh, Mark, Earney, Tait, Dean, Branden, Zielinski, Laurie, and Ritzinger, Brent. 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 ". United States. https://gdr.openei.org/submissions/1349.
@div{oedi_5792, 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, Glen, Jonathan, Siler, Drew, Faulds, James, Coolbaugh, Mark, Earney, Tait, Dean, Branden, 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.}, doi = {}, url = {https://gdr.openei.org/submissions/1349}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {06}}
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
Original Source
https://gdr.openei.org/submissions/1349Research Areas
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
