I-Corps: Translation Potential of a Seismic Monitoring System Empowered by Machine Learning Automation

Information

  • NSF Award
  • 2437766
Owner
  • Award Id
    2437766
  • Award Effective Date
    9/1/2024 - a year ago
  • Award Expiration Date
    8/31/2025 - 3 months ago
  • Award Amount
    $ 50,000.00
  • Award Instrument
    Standard Grant

I-Corps: Translation Potential of a Seismic Monitoring System Empowered by Machine Learning Automation

The broader impact of this I-Corps project is the development of a fully automated seismic monitoring system. The new seismic monitoring system will contribute to safer energy production and storage which may have significant, positive environmental and societal implications. The need for this solution is partially due to new technologies like hydraulic fracturing in oil, gas and geothermal reservoirs which cause earthquakes but also due to the need to safely store energy and carbon dioxide (CO2) in the sub-surface. Better seismic monitoring systems provide early alerts enabling reservoir operations to be adjusted to mitigate this hazard. The new technology will contribute to a safer and more efficient transition to new energy sources. <br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an automated seismic monitoring system. The need for seismic monitoring is rapidly accelerating both in plate boundary and energy producing areas. The increasingly widespread usage of geothermal energy and CO2 capture and sequestration is expected to be associated with significant seismic hazards which need to be effectively monitored. Seismic monitoring is complex, expensive, and time consuming, and many companies struggle with the right approach. The new technology will provide a comprehensive new seismic monitoring package, including instrumentation, software applications and visualization tools to facilitate a completely automated approach. The product builds on state-of-the-art machine learning processing pipelines and large amounts of labeled data from previous experiments. The new system is easy to deploy and maintain, which significantly cuts installation and personnel costs. The ultimate goal is to facilitate swift and safe energy storage and production through cutting-edge seismic monitoring.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

  • Program Officer
    Jaime A. Cameliojcamelio@nsf.gov7032922061
  • Min Amd Letter Date
    7/29/2024 - a year ago
  • Max Amd Letter Date
    7/29/2024 - a year ago
  • ARRA Amount

Institutions

  • Name
    University of Memphis
  • City
    MEMPHIS
  • State
    TN
  • Country
    United States
  • Address
    115 JOHN WILDER TOWER
  • Postal Code
    381520001
  • Phone Number
    9016783251

Investigators

  • First Name
    Thomas
  • Last Name
    Goebel
  • Email Address
    thgoebel@memphis.edu
  • Start Date
    7/29/2024 12:00:00 AM

Program Element

  • Text
    I-Corps
  • Code
    802300

Program Reference

  • Text
    ARTIFICIAL INTELL & COGNIT SCI
  • Code
    6856