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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.

Resources

Format	Description	Link
33	This 2025 report summarizes the progress of the Utah FORGE project 6-3656.	https://gdr.openei.org/files/1786/6-3656-LBNL%202025%20Annual%20Report.pdf
33	These are the slides presented at the 2025 Utah FORGE annual workshop for project 6-3656.	https://gdr.openei.org/files/1786/6-3656-LBNL%202025%20Annual%20Workshop%20Presentation.pdf
77	This is a presentation recording from the 2025 Utah FORGE annual workshop for project 6-3656.	https://gdr.openei.org/files/1786/LBNL_6-36-56%202025%20Annual%20Workshop%20Recording.mp4

Utah FORGE 6-3656: Real-Time Traffic Light System and Reservoir Engineering with Seismicity Forecasting and Ground Motion Prediction - 2025 Workshop Presentation

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