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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
7 Resources
0 Stars
Publicly accessible
7 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
4 Resources
0 Stars
Publicly accessible
PoroTomo: Brady Geothermal Field InSAR Data
Included are links to compressed InSAR pairs covering Brady Geothermal Field for TerraSAR-X (tracks 53, 91, and 167) and Sentinel-1A data. Pairs from the PoroTomo deployment period and pairs forming a minimum spanning tree according to perpendicular baseline are included for Terr...
Reinisch, E. University of Wisconsin
Jul 11, 2017
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible
Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data
Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence gr...
Reinisch, E. University of Wisconsin
Jul 28, 2016
1 Resources
0 Stars
Publicly accessible
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
1 Resources
0 Stars
Publicly accessible