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

Metadata Updated: September 18, 2024

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.

Access & Use Information

Public: This dataset is intended for public access and use. License: Creative Commons Attribution

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Dates

Metadata Created Date September 18, 2024
Metadata Updated Date September 18, 2024

Metadata Source

Harvested from OpenEI data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date September 18, 2024
Metadata Updated Date September 18, 2024
Publisher Energy and Geoscience Institute at the University of Utah
Maintainer
Doi 10.15121/2441446
Identifier https://data.openei.org/submissions/6196
Data First Published 2024-09-17T06:00:00Z
Data Last Modified 2024-09-17T16:44:10Z
Public Access Level public
Bureau Code 019:20
Metadata Context https://openei.org/data.json
Metadata Catalog ID https://openei.org/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Data Quality True
Harvest Object Id 3269e90c-227f-40eb-8169-17b47baa20b0
Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
Harvest Source Title OpenEI data.json
Homepage URL https://gdr.openei.org/submissions/1659
License https://creativecommons.org/licenses/by/4.0/
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Program Code 019:006
Projectlead Lauren Boyd
Projectnumber EE0007080
Projecttitle Utah FORGE
Source Datajson Identifier True
Source Hash 843b3172fee801cecdd5d802f3739422a1b92700838f73443f30c9acd33d6ff3
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