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

DOI 10.15121/1773692
Publicly accessible License 

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

AI-Geothermal_InputFiles.zip

Compressed file with all input files for training and testing the AI
58
Download 37.34 MB

b2d_classification.tif

Results of machine learning classification of Desert Peak using the Brady-trained model
47
Download 370.95 kB

brady_classification.tif

Results of machine learning classification of the Brady Springs-trained model
77
Download 104.52 kB

brady_som_output.grd

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
82
Download 0.5 kB

brady_som_output.grd.aux.xml

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
69
Download 3.46 kB

brady_som_output.grd.xml

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
63
Download 6.21 kB

brady_som_output.gri

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
108
Download 13.57 MB

brady_som_output.hdr

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
62
Download 0.79 kB

brady_som_output.stx

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
56
Download 0.03 kB

d2b_classification.tif

Results of machine learning classification of Brady using the Desert Peak-trained model
69
Download 101.29 kB

desert_classification.tif

Results of machine learning classification of the Desert Peak-trained model
57
Download 420.38 kB

desert_som_output.grd

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
78
Download 0.53 kB

desert_som_output.gri

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
82
Download 57.99 MB

desert_som_output.gri.aux.xml

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
76
Download 1.34 kB

desert_som_output.gri.ovr

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
44
Download 50.39 kB

desert_som_output.hdr

Labeled dataset, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model
63
Download 0.79 kB

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 - Open Energy Data Initiative (OEDI) 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 -
Export Citation to RIS
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, GDR. 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]. GDR. 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 GDR. https://doi.org/10.15121/1773692
@misc{OEDI_Dataset_7406, 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 = {GDR, Colorado School of Mines, https://doi.org/10.15121/1773692}, note = {Accessed: 2025-05-18}, 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

jmoraga@mines.edu

303.273.3768

Authors

Jim Moraga

Colorado School of Mines

Mahmut Cavur

Colorado School of Mines

H. Sebnem Duzgun

Colorado School of Mines

Hilal Soydan

Colorado School of Mines

Research Areas

DOE Project Details

Project Name Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning

Project Lead Mike Weathers

Project Number EE0008760

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