Subsurface Seismic Imaging Using Full-Waveform Inversion and Physics-Informed Neural Networks

Roadway subsidence presents a significant challenge in the maintenance and safety of transportation infrastructure. This localized downward movement of the ground surface is largely due to buried low-velocity anomalies, such as highly compressible soft clay or loose sand zones, voids, and abandoned mine workings. Subsidence not only compromises the integrity of the road surface but also poses a considerable risk to the safety of the traveling. The ability to effectively assess and address this geohazard is, therefore, a crucial aspect of transportation system management. The early identification of subsurface anomalies is key to mitigating risks associated with roadway subsidence. By detecting potential hazards before they manifest as surface deformations, remedial actions can be undertaken to prevent extensive damage or catastrophic collapse of the roadway. This proactive approach to roadway maintenance ensures the continuous safety and efficiency of transportation routes, thereby minimizing disruptions and potential hazards to the public. The overall objective of this research is to integrate Physics-Informed Neural Networks with full-waveform inversion to solve the elastic wave equation in heterogeneous geomaterials and invert subsurface low-velocity anomalies.

• Record URL:
  https://www.ctips.org/projects/details.php?id=607

Language

• English

Project

• Status: Active
• Funding: $100000
• Contract Numbers:

  69A3552348308

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Transformative Infrastructure Preservation and Sustainability

  North Dakota State University
  Fargo, ND  United States  58108
• Project Managers:

  Tolliver, Denver

• Performing Organizations:

  University of Utah

  Department of Civil and Environmental Engineering
  110 Central Campus Drive Suite 2000
  Salt Lake City, UT  United States  84112
• Principal Investigators:

  Mohammadi, Kami

• Start Date: 20240506
• Expected Completion Date: 20260505
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: CTIPS-009

Subject/Index Terms

• TRT Terms: Elastic waves; Geotechnical engineering; Highway maintenance; Image processing; Neural networks; Subsidence (Geology)
• Subject Areas: Data and Information Technology; Geotechnology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01920539
• Record Type: Research project
• Source Agency: Center for Transformative Infrastructure Preservation and Sustainability
• Contract Numbers: 69A3552348308
• Files: UTC, RIP
• Created Date: Jun 4 2024 2:11PM