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SSGF Mineral Major Elements and Lithium Concentration

May 01, 2023
1.38 MB
Publicly accessible
Presented are major element and lithium concentrations of minerals from the Salton Sea Geothermal Field (SSGF), located in the Imperial Valley, California. With a recent increase in demand for lithium, the area is now being studied as a source of geothermal brine for lithium extr...
Authors
Humphreys, J. et al Lawrence Berkeley National Laboratory
geothermallithiummineralrockssalton sea geothermal fieldbrinelithium isotopteschloritegeologygeochemistryhydrothermalisotope quantificationextractionsalton sealithium valleygeothermal brineimperial countyraw data

SSGF Trace Element Concentrations

Jan 08, 2026
706.57 kB
Awaiting release
Presented are trace element concentrations of minerals from the Salton Sea Geothermal Field (SSGF), located in the Imperial Valley, California. With a recent increase in demand for lithium, the area is actively being studied as a source of geothermal brine for lithium extraction. ...
Authors
Kovalick, A. et al University of California Riverside
geothermaltrace elementssalton sea geothermal fieldrocksmineralhydrothermallithium valleygeothermal brineraw dataimperial countyssgfimperial valleyrare earth elementsreecritical mineralsmnznconibrine-rock reactionrock samplesbrine samplesrhyolitic domesdurmid hillsschmitt and hulenca 2-14wellschemistryfluid chemistrygeochemistrysalton sealithium extraction

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

Jun 25, 2019
648.26 MB
Publicly accessible
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). ...
Authors
Reinisch, E. and Feigl, K. University of Wisconsin
geothermalenergyinsardemcoso geothermal fieldcaliforniaenvienvisats1asentinel-1aremote sensingcosotrack 349track 64pairgmt5sardigital elevation modelraw dataprocessed datageospatial dataphaseporotomocharacterizationhydrothermalhigh temperatureradar

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Oct 20, 2021
6.41 MB
Publicly accessible
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...
Authors
Beckers, K. et al National Renewable Energy Laboratory
geothermalenergymachine learningmlsubsurfacecharacterizationbrady hot springsbhspredictionreservoir modelingtime seriespcaprincipal component analysisreservoir managementreservoirdual-porositystimulationinjection testpdetemperatureflowpressuresimulationsingle-fracturedoubletheat maptensorflowcnnlstmmlphydrothermalopen source reservoirosrnevada