Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events
This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process must be done in real-time. A summary of the methodology is as follows: bandpass filter, shift (via cross-correlation) and stack signals, envelope function, peak detection, transfer function from amplitude to magnitude, creation of magnitude-frequency distribution, and finally, extract MFD "a" and "b" parameters. The datasets used in this work are linked below and include the raw waveform data and the seismic event catalog used for magnitude calibration, also hosted on the GDR.
Resources
3 resources available
-
Technical Report.pdf
PDF -
Seismic Event Catalogue from the April 2022 Stimulation
HTML -
2022 Well Stimulation Seismicity Data
HTML
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Jesse Williams", "@type": "vcard:Contact", "hasEmail": "mailto:jwilliams@gtcanalytics.com" } |
| dataQuality |
true
|
| description | This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process must be done in real-time. A summary of the methodology is as follows: bandpass filter, shift (via cross-correlation) and stack signals, envelope function, peak detection, transfer function from amplitude to magnitude, creation of magnitude-frequency distribution, and finally, extract MFD "a" and "b" parameters. The datasets used in this work are linked below and include the raw waveform data and the seismic event catalog used for magnitude calibration, also hosted on the GDR. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Technical Report.pdf", "format": "pdf", "accessURL": "https://gdr.openei.org/files/1705/FORGE_milestone_1p3_19_1_2025.pdf", "mediaType": "application/pdf", "description": "This report proposes to develop a predictive tool to estimate the future magnitude of induced seismicity using artificial intelligence (AI) and physics-based modeling. The sections detail the progress made in obtaining time-series data with identified events, providing a foundation for constructing these predictive models." }, { "@type": "dcat:Distribution", "title": "Seismic Event Catalogue from the April 2022 Stimulation", "format": "HTML", "accessURL": "https://gdr.openei.org/submissions/1399", "mediaType": "text/html", "description": "This dataset includes earthquake catalogues for the three stages of the 2022 well 16A(78)-32 stimulation provided by Geo Energie Suisse. Events in these catalogues have been visually inspected. There are additional events of lower signal to noise that were automatically detected. Those events will require additional analysis and processing. Times are recorded in UTC (Coordinate Universal Time), and the coordinate reference system is UTM Zone 12N, NAD83." }, { "@type": "dcat:Distribution", "title": "2022 Well Stimulation Seismicity Data", "format": "HTML", "accessURL": "https://gdr.openei.org/submissions/1494", "mediaType": "text/html", "description": "This is a link to the Utah FORGE seismic data distribution site hosted by the University of Utah Seismograph Stations. The data was collected from downhole geophone strings in wells 56-32, 58-32 and 78B-32 during the 2022 stimulation of well 16A(78)-32. This dataset, which was updated in April 2023, now contains SeGY formatted data." } ] |
| identifier | https://data.openei.org/submissions/8318 |
| issued | 2025-01-21T07:00:00Z |
| keyword |
[ "AI", "EGS", "ML", "Utah FORGE", "artificial intelligence", "borehole seismic", "data processing", "energy", "event catalog", "event detection", "geophysics", "geothermal", "induced seismicity", "machine learning", "magnitude-frequency distribution", "microseismic", "physics informed", "real-time", "recurrent neural networks", "seismic data", "technical report" ] |
| landingPage | https://gdr.openei.org/submissions/1705 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-01-22T21:04:55Z |
| programCode |
[ "019:006" ] |
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Utah FORGE |
| publisher |
{ "name": "Global Technology Connection, Inc.", "@type": "org:Organization" } |
| spatial |
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|
| title | Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events |
