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PoroTomo: Horizontal Distributed Acoustic Sensing (DAS) Measurements During an M 2.3 Explosion

Included here are Distributed Acoustic Sensing (DAS) data collected by the horizontal DAS array at Brady's Hot Springs Geothermal Field. The system recorded this data during an M 2.3 explosion at the Nevada Test Site (NTS), which is located approximately 400km southeast of the fie...

Kratt, C. et al Center for Transformative Environmental Monitoring Programs (CTEMPs)

Dec 18, 2018
5 Resources
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Material Properties for Brady Hot Springs Nevada USA from PoroTomo Project

The PoroTomo team has completed inverse modeling of the three data sets (seismology, geodesy, and hydrology) individually, as described previously. The estimated values of the material properties are registered on a three-dimensional grid with a spacing of 25 meters between nodes....

Feigl, K. and PoroTomo Team, . University of Wisconsin

Mar 06, 2019
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Envisat Track 349 and Sentinel-1A Track 64 Interferometric Synthetic Aperture Radar Data of Coso Geothermal Field, California, USA, 2004-2016

This submission contains tarred pair directories for interferometric synthetic aperture radar (InSAR) data covering Coso Geothermal Field in California, USA. Explanation of pair subdirectories: Pairs are formed using the InSAR processing software GMT5SAR (Sandwell et al., 2011). ...

Reinisch, E. and Feigl, K. University of Wisconsin

Jun 25, 2019
4 Resources
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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
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Curated

Brady's Geothermal Field Distributed Temperature Sensing Data

This submission is an 8 day time history of vertical temperature measurements in Brady observation well 56-1 collected during the PoroTomo field experiment. The data was collected with a fiber-optic DTS system installed to a depth of 372 m below wellhead. DTS installation uses a d...

Patterson, J. University of Wisconsin

Mar 26, 2016
2 Resources
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Understanding Constraints on Geothermal Sustainability Through Reservoir Characterization at Brady Geothermal Field, Nevada

The vast supply of geothermal energy stored throughout the Earth and the exceedingly long time required to dissipate that energy makes the world's geothermal energy supply nearly limitless. As such, this resource holds the potential to provide a large supply of the world's energy ...

Patterson, J. University of Wisconsin

Jul 11, 2018
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Distributed Acoustic Sensing Experiment Data from Garner Valley, California

In September 2013, an experiment using Distributed Acoustic Sensing (DAS) was conducted at Garner Valley, a test site of the University of California Santa Barbara (Lancelle et al., 2014). This submission includes all DAS data recorded during the experiment. The sampling rate ...

Lancelle, C. University of Wisconsin

Sep 11, 2013
14 Resources
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Brady Geothermal Field Borehole Pressure Data

This submission supersedes pressure data from March 2017 which can be found as a link in the submission resources. This submission contains 3 .csv files with time series pressure data in 3 observation wells at Brady Geothermal Field as part of the PoroTomo project. These pressure ...

Cardiff, M. and Lim, D. University of Wisconsin

Apr 01, 2016
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...

Beckers, K. et al National Renewable Energy Laboratory

Feb 18, 2021
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