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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.
Resources
1 resource available
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USGS Contributions to the Nevada Geothermal Machine Learning Project
HTML
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "James Faulds",
"@type": "vcard:Contact",
"hasEmail": "mailto:jfaulds@unr.edu"
}
|
| dataQuality |
true
|
| description | 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "USGS Contributions to the Nevada Geothermal Machine Learning Project",
"format": "HTML",
"accessURL": "https://doi.org/10.5066/P9V5SQRD",
"mediaType": "text/html",
"description": "Link to USGS ScienceBase Catalog page for the USGS's contributions to the Nevada Geothermal Machine Learning Project. These contributions included geophysics, heat flow, and slip and dilation tendency data"
}
]
|
| identifier | https://data.openei.org/submissions/7463 |
| issued | 2021-06-01T06:00:00Z |
| keyword |
[
"Dilation",
"Heat Flow",
"Nevada",
"Slip",
"characterization",
"energy",
"exploration",
"faults",
"geophisics",
"geophysics",
"geothermal",
"geotiffs",
"gravity",
"great basin",
"hydrothermal",
"machine learning",
"magnetics",
"pfa"
]
|
| landingPage | https://gdr.openei.org/submissions/1349 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2022-10-07T20:33:25Z |
| programCode |
[
"019:006"
]
|
| projectLead | Mike Weathers |
| projectNumber | EE0008762 |
| projectTitle | Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada |
| publisher |
{
"name": "Nevada Bureau of Mines and Geology",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-119.826371875,38.35910638319357],[-114.3442953125,38.35910638319357],[-114.3442953125,41.897579646871705],[-119.826371875,41.897579646871705],[-119.826371875,38.35910638319357]]]}"
|
| 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 |
