Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks - 2024 Annual Workshop Presentation

Description

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.

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Name Format Description Link
77 As part of the 2024 Utah FORGE R&D Workshop, this presentation offers the newest updates to the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks project from GTC Analytics. The presentation follows a standard format, with a 15 minute presentation section followed by a 10 minute Q&A via Utah FORGE panelists and the presenters. https://gdr.openei.org/files/1659/GlobalTechConnection%206-3712%20GMT20240815-143041_Recording_as_2560x1080.mp4

Tags

  • seismicity-predictor
  • multi-frequency
  • egs
  • deep-learning
  • utah-forge
  • seismicity
  • dl
  • stimulation
  • seismic
  • stimulation-induced-seismicity
  • predictive-systems
  • presentation
  • magnitude-frequency-distribution
  • video
  • machine-learning
  • geothermal
  • energy

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