Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This submission contains shapefiles, geotiffs, and symbology for the revised-from-Play-Fairway potential structures/structural settings used in the Nevada Geothermal Machine Learning project. Layers include potential structural setting ellipses, centroids, and distance-to-centroid raster.
A submission linking the full GitHub repository for our machine learning Jupyter Notebooks will appear in the related datasets section of this page once available.
Citation Formats
Nevada Bureau of Mines and Geology. (2021). Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada [data set]. Retrieved from https://dx.doi.org/10.15121/1832125.
Faulds, James, Coolbaugh, Mark. Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada. United States: N.p., 20 Feb, 2021. Web. doi: 10.15121/1832125.
Faulds, James, Coolbaugh, Mark. Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada. United States. https://dx.doi.org/10.15121/1832125
Faulds, James, Coolbaugh, Mark. 2021. "Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada". United States. https://dx.doi.org/10.15121/1832125. https://gdr.openei.org/submissions/1353.
@div{oedi_1353, title = {Potential structures - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada}, author = {Faulds, James, Coolbaugh, Mark.}, abstractNote = {This submission contains shapefiles, geotiffs, and symbology for the revised-from-Play-Fairway potential structures/structural settings used in the Nevada Geothermal Machine Learning project. Layers include potential structural setting ellipses, centroids, and distance-to-centroid raster.
A submission linking the full GitHub repository for our machine learning Jupyter Notebooks will appear in the related datasets section of this page once available.}, doi = {10.15121/1832125}, url = {https://gdr.openei.org/submissions/1353}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {02}}
https://dx.doi.org/10.15121/1832125
Details
Data from Feb 20, 2021
Last updated Feb 9, 2022
Submitted Nov 16, 2021
Organization
Nevada Bureau of Mines and Geology
Contact
James Faulds
775.682.8751
Authors
Keywords
geothermal, energy, Nevada, Machine Learning, Structure, Potential Structures, Structural Setting, Accommodation Zone, Displacement Transfer Zone, Fault Bend, Fault Intersection, Fault Termination, Pull Apart, Stepover, geospatial data, geospatial, data, code, ellipses, centroids, distance to centroid, gis, rasterDOE Project Details
Project Name Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
Project Lead Mike Weathers
Project Number EE0008762
