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PoroTomo: BRAD and BRDY GPS Station RINEX Files December 20, 2014

This dataset provides links to daily RINEX files for two GPS stations at Brady's Hot Springs as of December 20, 2014. The data is formatted as compressed GNSS RINEX observation files and is accessible through CSV files containing metadata such as file size, data type, and publicat...

Kreemer, C. University of Nevada

Dec 20, 2014
4 Resources
0 Stars
Curated

PoroTomo: BRAD and BRDY GPS Station RINEX Files March 26, 2015

This dataset provides links to daily RINEX files for two GPS stations at Brady's Hot Springs as of March 26, 2015. The data is formatted as compressed GNSS RINEX observation files, accessible through formatted CSV files, as well as in links to the complete daily time series data. ...

Kreemer, C. University of Wisconsin

Jan 01, 2015
7 Resources
0 Stars
Curated

Newberry EGS Literature References

Research references to literature about the Newberry geothermal area, Oregon.

Rose, K. National Energy Technology Laboratory

Apr 22, 2016
1 Resources
0 Stars
Publicly accessible

PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data

This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also include...

Feigl, K. et al University of Wisconsin

Mar 29, 2016
20 Resources
1 Stars
Curated

DASH Slow Strain Rates from Brady Hot Springs Geothermal Field during PoroTomo Deployment Period

This submission contains slow strain rates summed to radians over 30 second intervals [rad/s] derived from horizontal distributed acoustic sensing measurements (DASH) of Brady geothermal field during PoroTomo deployment (2016-Mar-14 to 2016-Mar-26). There is one file correspondin...

Reinisch, E. et al University of Wisconsin

Jun 27, 2018
20 Resources
0 Stars
Curated

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...

Duzgun, H. et al Colorado School of Mines

Jan 08, 2021
12 Resources
0 Stars
Publicly accessible

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...

Beckers, K. et al National Renewable Energy Laboratory

Oct 20, 2021
6 Resources
0 Stars
Publicly accessible