AI Initiative Enhances Real-Time Seismic Monitoring and Workforce Development in West Texas and New Mexico

New AI Project Enhances Real-Time Seismic Monitoring in West Texas and New Mexico

An exciting new project is underway to improve seismic monitoring and forecasting using advanced machine learning (ML) techniques. Supported by a $1.8 million grant from the U.S. Department of Energy (DOE) Technology Commercialization Fund (TCF), Lawrence Livermore National Laboratory (LLNL) is leading the initiative in the West Texas/New Mexico region.

The project, titled "Advancing Real-Time Forecasts of Induced Seismicity with Machine Learning-Based Event Detection and Location", aims to produce practical solutions for managing induced seismicity—earthquakes caused by human activities. This is critical for industries such as carbon storage, oil and gas production, and geothermal energy.

Collaboration and Community Engagement

LLNL is partnering with Oak Ridge National Laboratory, Instrumental Software Technologies, Inc. (ISTI), the Livermore Lab Foundation, and several local community colleges, including Southeast New Mexico College, Midland College, and Odessa College in Texas. The project is also known as RECONNECT (Real-time inducEd seismiCity fOrecasts learNiNg sEismic CaTalogs).

The community colleges will host Energy Workforce events featuring workshops and expert panels to educate people about emerging energy technologies and career opportunities. Paid summer internships at LLNL will be offered through the Livermore Lab Foundation to support student engagement in STEM fields. Outreach efforts are planned to begin in fall 2025.

Joshua Hardt of Odessa College emphasized the value of this partnership: "This will equip the West Texas community with essential tools and data, enhancing our engagement with seismic science and safety." Richard Wiedenmann of Southeast New Mexico College added, "Educating the public about frequent earthquakes benefits both the community and our students in science, engineering, and workforce development."

Technology and Impact

The RECONNECT initiative addresses challenges in current seismic monitoring systems. Traditional techniques often struggle in noisy industrial environments, causing delays and inaccuracies in detecting seismic events. The project will develop a real-time ML pipeline integrated with the DOE's Operational Forecasting of Induced Seismicity (ORION) toolkit.

It also focuses on creating high-precision historic seismic event catalogs to provide deeper insights into local and regional seismic activity. This will help operators anticipate and mitigate risks before starting operations.

The ML tools developed with ISTI’s collaboration will offer cost-effective, advanced monitoring and forecasting capabilities, allowing operators to make informed, timely decisions. These improvements benefit any industrial operations involving fluid injection or extraction from underground formations.

Supporting Safety and Confidence

The project’s primary goal is to improve operational safety and public confidence in energy technologies. Accurate detection, classification, and forecasting of seismic activity enable operators to manage risks proactively.

The DOE TCF supports technology commercialization from national labs by fostering public-private partnerships like this one. LLNL’s Innovation and Partnerships Office helps translate lab innovations into practical applications that contribute to economic competitiveness and national security.

For IT and development professionals interested in AI applications in energy and environmental monitoring, this project highlights the growing role of ML in critical infrastructure. It also shows the value of collaboration between national labs, private companies, and educational institutions.

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