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Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2026 Workshop Presentation

Sep 30, 2026
73.58 MB
Curated
This is a presentation on Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics (Global Technology Connection), presented by Dr. Jesse Williams. The project is developing machine-learning and physics-informed models to impro...
Authors
Williams, J. GTC Analytics
geothermalenergyutah forgeegs2026 annual workshopmachine-learningneural networksphysics basedphysics-informedseismic responseinduced seismicityforecastingstimulation-induced seismicitytraffic lightgeothermal reservoirreservoir managementpresentationreportdecision support

QuakeCastNet: Source Code for Forecasting of Injection-Induced Seismicity

Sep 20, 2026
16.21 MB
Curated
This submission houses the companion code for the manuscript by Zhengfa Bi (Lawrence Berkeley National Laboratory) and Nori Nakata (Lawrence Berkeley National Laboratory; MIT). Both the companion code and manuscript are included in the resources section of this submission. Fluid ...
Authors
Nakata, N. and Bi, Z. Lawrence Berkeley National Laboratory
geothermalenergyutah forgeseismicitydeep learningthe geysersforecastegsinduced seismicityseismicity forecastingtftprobabilisticmulti-horizontemporal transformer frameworkmachine learningdecision supporttraffic light