Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

Published by Colorado School of Mines | Department of Energy | Metadata Last Checked: January 27, 2026 | Last Modified: 2021-05-17T16:03:00Z

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model

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Complete Metadata

@type	dcat:Dataset
accessLevel	public
bureauCode	[ "019:20" ]
contactPoint	{ "fn": "Jim Moraga", "@type": "vcard:Contact", "hasEmail": "mailto:jmoraga@mines.edu" }
dataQuality	true
description	The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model
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DOI	10.15121/1773692
identifier	https://data.openei.org/submissions/7406
issued	2020-09-01T06:00:00Z
keyword	[ "Brady Hot Springs", "Desert Peak", "Fallon", "GeoTIFF", "Nevada", "PSInSAR", "Subsidence", "Uplift", "convolutional neural network", "energy", "fault density", "geospatial data", "geothermal", "geothermal exploration", "hydrothermal", "hydrothermal mineral alterations", "land surface temperature", "machine learning", "mineral", "model", "raster", "temperature" ]
landingPage	https://gdr.openei.org/submissions/1288
license	https://creativecommons.org/licenses/by/4.0/
modified	2021-05-17T16:03:00Z
programCode	[ "019:006" ]
projectLead	Mike Weathers
projectNumber	EE0008760
projectTitle	Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
publisher	{ "name": "Colorado School of Mines", "@type": "org:Organization" }
spatial	"{"type":"Polygon","coordinates":[[[-119.2167,39.55],[-118.75,39.55],[-118.75,39.9883],[-119.2167,39.9883],[-119.2167,39.55]]]}"
title	Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak