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  "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": [
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      "@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"
    },
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      "@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"
  },
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  "title": "Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation"
}