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Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation
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National Renewable Energy Laboratory (NREL) - view all
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Overview

This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to estimate the magnitude-frequency response of stimulation-induced seismicity. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

DLEGSUtah FORGEdeep learningenergygeothermalmachine learningmagnitude-frequency distributionmulti frequencypredictive systemspresentationseismicseismicityseismicity predictorstimulationstimulation-induced seismicityvideo
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Dcat Issued2024-09-17T06:00:00Z
Dcat Modified2024-09-17T16:44:10Z
Dcat Publisher NameEnergy and Geoscience Institute at the University of Utah
Guidhttps://data.openei.org/submissions/7728
Harvest Object Idd9ec7fea-7b3c-4f8f-99c7-14fa1fbce094
Harvest Source Id4eb7107f-a2b1-40e3-b36a-8161aa98a56e
Harvest Source TitleOpenEI Data Portal
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