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Bradys Hot Springs Ambient Noise Correlation Functions (Initial Waveforms)

These files are ambient noise correlation (ANC) functions calculated for 11 days of continuous seismic data recorded by the Lawrence Berkeley network in the Brady geothermal field. These are SAC formatted seismic waveforms. The stations included are BPB04, BPB05, BPB07, BPB08, BP...

Matzel, E. Lawrence Livermore National Laboratory

Jul 01, 2015
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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
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Analysis of Existing Data from a Distributed Acoustic Sensing Experiment at 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 noise cross-correlation functions (NCF) . Each file includes a NCF ...

Zeng, X. University of Wisconsin

Mar 26, 2015
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Active Source 3D Seismic Tomography of Brady Hot Springs Geothermal Field, Nevada

We deployed a dense seismic array to image the shallow structure in the injection area of the Brady Hot Springs geothermal site in Nevada. The array was composed of 238 5 Hz, three-component nodal instruments and 8,700 m of distributed acoustic sensing (DAS) fiber-optic cable inst...

Parker, L. University of Wisconsin

Aug 09, 2017
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Porotomo: InSAR Data from San Emidio Geothermal Field, Nevada, 1992-2010

This submission contains tarred pair directories for interferometric synthetic aperture radar (InSAR) data covering San Emidio Geothermal Field in Nevada, USA as part of the porotomo project. Data included within this submission are the following: > ENVI_T120_GDR.tgz: Tarred direc...

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

Jun 25, 2019
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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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Analysis of Existing InSAR Data Brady Hot Springs Geothermal Area

Attributes of synthetic aperture radar (SAR) data acquired by TerraSAR-X and TanDEM-X satellite missions and archived at WInSAR facility.

Feigl, K. University of Wisconsin

Dec 26, 2014
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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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Guelph, ON Canada DTS Metadata

Metadata for active distributed temperature survey (DTS) experiments at Guelph, Ontario Canada. This data that this metadata refers to was taken as part of the PoroTomo project. The metadata includes information about status, location, elevation, units, and other metadata.

Coleman, T. University of Wisconsin

Dec 15, 2014
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Analyzed Boise Data for Oscillatory Hydraulic Tomography

Data here has been "pre-processed" and "analyzed" from the raw data submitted to the GDR previously (raw data files found at http://gdr.openei.org/submissions/479. doi:10.15121/1176944 after 30 September 2017). First, we submit .mat files which are the "pre-processed" data (must h...

Lim, D. University of Wisconsin

Jul 01, 2015
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Brady's Geothermal Field Analysis of Pressure Data

*This submission provides corrections to GDR Submissions 844 and 845* Poroelastic Tomography (PoroTomo) by Adjoint Inverse Modeling of Data from Hydrology. The 3 *csv files containing pressure data are the corrected versions of the pressure dataset found in Submission 844. The ...