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Utah FORGE 6-3712: Report on Building a Recurrent Neural Network Framework for Induced Seismicity - October, 2025

Metadata Updated: October 14, 2025

This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent neural network (RNN) to predict induced seismicity. Background material is included to inform non-subject matter experts about the types of architectures available. The exact architectures (layers) of three models are discussed, which are being used to predict induced seismicity.

Access & Use Information

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

Downloads & Resources

Dates

Metadata Created Date October 14, 2025
Metadata Updated Date October 14, 2025

Metadata Source

Harvested from OpenEI data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date October 14, 2025
Metadata Updated Date October 14, 2025
Publisher Global Technology Connection, Inc.
Maintainer
Identifier https://data.openei.org/submissions/8550
Data First Published 2025-10-13T06:00:00Z
Data Last Modified 2025-10-13T19:39:01Z
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 942ded9e-a7c4-4cda-b94c-fc99a87a19fd
Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
Harvest Source Title OpenEI data.json
Homepage URL https://gdr.openei.org/submissions/1797
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 528183ca5e582ff52107c48aec5f562dbb83801644cbcb975eebfdc83b9f6b38
Source Schema Version 1.1
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