OpenEI: Energy Information
  • Geothermal Data Repository
  • My User
    • Sign Up
    • Login
 
  • Data
    • View All Submissions
    • Data Lakes
    • Data Standards
    • Submit Data
  • Help
    • Frequently Asked Questions
    • Data Submission Best Practices
    • Data Submission Tutorial Videos
    • Instructions for Funds Recipients
    • Data Provision Guidelines
    • Contact GDR Help
  • About
  • Search

Search GDR Data

Showing results 1 - 7 of 7.
Show results per page.
Order by:
Available Now:
Filters Clear All Filters ×
Technologies
Featured Projects
Topics
Data Type
"high-resolution"×
PoroTomo×

PoroTomo Distributed Temperature Sensing (DTS) Measurements made in Brady Observation Well 56-1

This submission is a follow-up to Distributed Temperature Sensing (DTS) measurements made in Brady observation well 56-1 during the PoroTomo field experiment conducted in March, 2016. The measurements in this data set were made on August 24, 2018 over an approximately 20 hour per...
Kratt, C. et al Oregon State University
Jan 09, 2019
6 Resources
0 Stars
Publicly accessible

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
1 Resources
0 Stars
Publicly accessible

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
1 Resources
0 Stars
Publicly accessible

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
1 Resources
0 Stars
Publicly accessible

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
0 Stars
Publicly accessible

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
10 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
Publicly accessible
  • About the GDR
  • Partners & Sponsors
  • Disclaimers
  • Developer Services
  • The GDR is the submission point for all data collected from research funded by the U.S. Department of Energy's Geothermal Technologies Office.
  • Content is available under Creative Commons Attribution 4.0 unless otherwise noted.

Privacy Policy Notification

This site uses cookies to store and share user preferences with other OpenEI sites, and uses Google Analytics to collect anonymous user information such as which pages are visited, for how often, and what searches or other webpages may have led users here. You can prevent Google Analytics from recognizing you on return visits to this site by disabling cookies on your browser or by installing a Google Analytics Opt-out Browser Add-on. By clicking "Accept" you agree this site can store cookies on your device and disclose information to OpenEI and Google Analytics in accordance with our privacy policy.

OpenEI Privacy Policy Google Analytics Terms of Service