USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics, Heat Flow, Slip and Dilation Tendency
This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project (DE-FOA-0001956), 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?, 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.
|USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics, Heat Flow, Slip and Dilation Tendency
|Jacob DeAngelo, Jonathan M Glen, Drew L Siler, Mark F. Coolbaugh, Tait E Earney, Branden J Dean, Laurie A Zielinski, Brent T. Ritzinger
|USGS Digital Object Identifier Catalog
|Geology, Minerals, Energy, and Geophysics Science Center