Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation
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.
Citation Formats
TY - DATA
AB - 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.
AU - Nakata, Nori
DB - Geothermal Data Repository
DP - Open EI | National Laboratory of the Rockies
DO -
KW - geothermal
KW - energy
KW - Utah FORGE
KW - 2025 Annual Workshop
KW - EGS
KW - induced seismicity
KW - traffic light system
KW - machine learning
KW - seismicity
KW - forecasting
KW - ground motion prediction
KW - generative AI
KW - reservoir engineering
KW - high-pressure experiments
KW - presentation
KW - presentation slides
KW - presentation recording
KW - report
LA - English
DA - 2025/09/18
PY - 2025
PB - Lawrence Berkeley National Laboratory
T1 - Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation
UR - https://gdr.openei.org/submissions/1786
ER -
Nakata, Nori. Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation. Lawrence Berkeley National Laboratory, 18 September, 2025, Geothermal Data Repository. https://gdr.openei.org/submissions/1786.
Nakata, N. (2025). Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation. [Data set]. Geothermal Data Repository. Lawrence Berkeley National Laboratory. https://gdr.openei.org/submissions/1786
Nakata, Nori. Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation. Lawrence Berkeley National Laboratory, September, 18, 2025. Distributed by Geothermal Data Repository. https://gdr.openei.org/submissions/1786
@misc{GDR_Dataset_1786,
title = {Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation},
author = {Nakata, Nori},
abstractNote = {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.},
url = {https://gdr.openei.org/submissions/1786},
year = {2025},
howpublished = {Geothermal Data Repository, Lawrence Berkeley National Laboratory, https://gdr.openei.org/submissions/1786},
note = {Accessed: 2026-07-28}
}
Details
Data from Sep 18, 2025
Last updated Sep 21, 2025
Submitted Sep 18, 2025
Organization
Lawrence Berkeley National Laboratory
Contact
Nori Nakata
Authors
Keywords
geothermal, energy, Utah FORGE, 2025 Annual Workshop, EGS, induced seismicity, traffic light system, machine learning, seismicity, forecasting, ground motion prediction, generative AI, reservoir engineering, high-pressure experiments, presentation, presentation slides, presentation recording, reportDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
