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 AU - Glen, Jonathan AU - Siler, Drew AU - Faulds, James AU - Coolbaugh, Mark AU - Earney, Tait AU - Dean, Branden AU - Zielinski, Laurie AU - Ritzinger, Brent DB - Open Energy Data Initiative (OEDI) 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://data.openei.org/submissions/ ER -