{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Nori Nakata", "hasEmail": "mailto:nnakata@lbl.gov"}, "dataQuality": true, "description": "This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This video slide presentation outlines the development of a near-real-time Adaptive Traffic Light System (ATLS) that combines machine-learning seismicity forecasting, generative AI ground-motion prediction, and high-pressure laboratory experiments to improve induced seismicity forecasting and reservoir engineering for Enhanced Geothermal Systems (EGS). This presentation was featured at the Utah FORGE R&D Annual Workshop on September 9, 2025. The workshop offered a valuable opportunity to review the progress of Research and Development projects funded under Solicitation 2022-2, which aim to improve our understanding of the key factors influencing Enhanced Geothermal System (EGS) reservoir and resource development.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1786/6-3656-LBNL%202025%20Annual%20Report.pdf", "description": "This 2025 report summarizes the progress of the Utah FORGE project 6-3656.", "format": "pdf", "mediaType": "application/pdf", "title": "6-3656 - 2025 Annual Report.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1786/6-3656-LBNL%202025%20Annual%20Workshop%20Presentation.pdf", "description": "These are the slides presented at the 2025 Utah FORGE annual workshop for project 6-3656.", "format": "pdf", "mediaType": "application/pdf", "title": "Presentation Slides.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1786/LBNL_6-36-56%202025%20Annual%20Workshop%20Recording.mp4", "description": "This is a presentation recording from the 2025 Utah FORGE annual workshop for project 6-3656.", "format": "mp4", "mediaType": "application/octet-stream", "title": "Presentation Recording.mp4"}], "identifier": "https://data.openei.org/submissions/8530", "issued": "2025-09-18T06:00:00Z", "keyword": ["2025 Annual Workshop", "EGS", "Utah FORGE", "energy", "forecasting", "generative AI", "geothermal", "ground motion prediction", "high-pressure experiments", "induced seismicity", "machine learning", "presentation", "presentation recording", "presentation slides", "report", "reservoir engineering", "seismicity", "traffic light system"], "landingPage": "https://gdr.openei.org/submissions/1786", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2025-09-21T20:38:55Z", "programCode": ["019:006"], "projectLead": "Lauren Boyd", "projectNumber": "EE0007080", "projectTitle": "Utah FORGE", "publisher": {"@type": "org:Organization", "name": "Lawrence Berkeley National Laboratory"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-112.916367,38.483935],[-112.879748,38.483935],[-112.879748,38.5148],[-112.916367,38.5148],[-112.916367,38.483935]]]}", "title": "Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation"}