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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

May 14, 2021
6.01 MB
Publicly accessible
Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...
Authors
Marone, C. et al Pennsylvania State University
geothermalenergymatlabgeophysicsseismiccodeprocessed datamicroseismicityaiartificial intelligencedeep learningmachine learningacousticsacoustic emissionsseismic forcastingseismic predictionfaultfault propertiesshear stresstime to failureexperimentexperimental datalab databiaxial shear experimentbiaxial shear apparatusfriction

Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

Feb 07, 2023
Size unavailable
Publicly accessible
Previous moderate and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable mode...
Authors
Mordensky, S. et al United States Geological Survey
geothermalenergypfahydrothermalenergy assessmentresource assessmentretrospectivemachine learninggeosciencewestern usdata-drivenbias reductionfavorabilitymappingegslow tempprocessed dataresourcecharacterization