USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Heat Flow Data
Dates
Publication Date
2021-11-30
Time Period
2021-07-01
Citation
DeAngelo, J., and Coolbaugh, M.F., 2021, USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Heat Flow Data: U.S. Geological Survey data release, https://doi.org/10.5066/P9HRX1LR.
Summary
This package contains a map surface that depicts the estimated spatial variation of conductive heat flow (mW/m²) in a portion of northern Nevada, the extent of the ‘Nevada Machine Learning Project’ (DE-EE0008762). It was generated using well locations that had an estimated heat flow value from a measured thermal gradient and thermal conductivity, mainly using data from Southern Methodist University, with some additional USGS data. Well data are included along with and a map surface depicting estimated standard error of the heat flow interpolation.
Summary
This package contains a map surface that depicts the estimated spatial variation of conductive heat flow (mW/m²) in a portion of northern Nevada, the extent of the ‘Nevada Machine Learning Project’ (DE-EE0008762). It was generated using well locations that had an estimated heat flow value from a measured thermal gradient and thermal conductivity, mainly using data from Southern Methodist University, with some additional USGS data. Well data are included along with and a map surface depicting estimated standard error of the heat flow interpolation.
These data were produced by USGS and collaborators as contributions to the DOE-funded Nevada Geothermal Machine Learning Project (DE-FOA-0001956). These data were used as possible inputs in an analysis that predicted geothermal favorability in a portion of northern Nevada.
