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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

Download All Resources 109.94 MB — 16 files

AI-Geothermal_InputFiles.zip

Compressed file with all input files for training and testing the AI

Download 37.34 MB

b2d_classification.tif

Results of machine learning classification of Desert Peak using the Brady-trained model

Download 370.95 kB

brady_classification.tif

Results of machine learning classification of the Brady Springs-trained model

120

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

120

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

104

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

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

134

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

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

Download 0.03 kB

d2b_classification.tif

Results of machine learning classification of Brady using the Desert Peak-trained model

Download 101.29 kB

desert_classification.tif

Results of machine learning classification of the Desert Peak-trained model

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

104

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

110

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

101

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

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

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  - 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  -

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, 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

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

DOE Project Details

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

Project Lead Mike Weathers

Project Number EE0008760

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

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