Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak
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
Citation Formats
TY - DATA
AB - 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
AU - Moraga, Jim
A2 - Cavur, Mahmut
A3 - Duzgun, H. Sebnem
A4 - Soydan, Hilal
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1773692
KW - geothermal
KW - energy
KW - geothermal exploration
KW - hydrothermal mineral alterations
KW - land surface temperature
KW - fault density
KW - PSInSAR
KW - Subsidence
KW - Uplift
KW - Brady Hot Springs
KW - Desert Peak
KW - Nevada
KW - convolutional neural network
KW - Fallon
KW - machine learning
KW - model
KW - hydrothermal
KW - mineral
KW - temperature
KW - raster
KW - geospatial data
KW - GeoTIFF
LA - English
DA - 2020/09/01
PY - 2020
PB - Colorado School of Mines
T1 - Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak
UR - https://doi.org/10.15121/1773692
ER -
Moraga, Jim, et al. Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak. Colorado School of Mines, 1 September, 2020, Geothermal Data Repository. https://doi.org/10.15121/1773692.
Moraga, J., Cavur, M., Duzgun, H., & Soydan, H. (2020). Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak. [Data set]. Geothermal Data Repository. Colorado School of Mines. https://doi.org/10.15121/1773692
Moraga, Jim, Mahmut Cavur, H. Sebnem Duzgun, and Hilal Soydan. Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak. Colorado School of Mines, September, 1, 2020. Distributed by Geothermal Data Repository. https://doi.org/10.15121/1773692
@misc{GDR_Dataset_1288,
title = {Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak},
author = {Moraga, Jim and Cavur, Mahmut and Duzgun, H. Sebnem and Soydan, Hilal},
abstractNote = {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},
url = {https://gdr.openei.org/submissions/1288},
year = {2020},
howpublished = {Geothermal Data Repository, Colorado School of Mines, https://doi.org/10.15121/1773692},
note = {Accessed: 2025-04-25},
doi = {10.15121/1773692}
}
https://dx.doi.org/10.15121/1773692
Details
Data from Sep 1, 2020
Last updated May 17, 2021
Submitted Feb 19, 2021
Organization
Colorado School of Mines
Contact
Jim Moraga
303.273.3768
Authors
Keywords
geothermal, energy, geothermal exploration, hydrothermal mineral alterations, land surface temperature, fault density, PSInSAR, Subsidence, Uplift, Brady Hot Springs, Desert Peak, Nevada, convolutional neural network, Fallon, machine learning, model, hydrothermal, mineral, temperature, raster, geospatial data, GeoTIFFDOE Project Details
Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning
Project Lead Mike Weathers
Project Number EE0008760