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Sinkhole collapse may result in significant property damage and even loss of life. Early detection of sinkhole attributes (buried voids, raveling zones) is critical to limit the cost of remediation. One of the most promising ways to obtain subsurface imaging is 3D seismic full-waveform inversion. For demonstration, a recently developed 3D Gauss-Newton full-waveform inversion (3D GN-FWI) method is used to detect buried voids, raveling soils, and characterize variable subsurface soil/rock layering. It is based on a finite-difference solution of 3D elastic wave equations and Gauss-Newton optimization. The method is tested first on a data set constructed from the numerical simulation of a challenging synthetic model and subsequently on field data collected from two separate test sites in Florida. For the field tests, receivers and sources are placed in uniform 2D surface grids to acquire the seismic wavefields, which then are inverted to extract the 3D subsurface velocity structures. The inverted synthetic results suggest that the approach is viable for detecting voids and characterizing layering. The field seismic results reveal that the 3D waveform analysis identified a known manmade void (plastic culvert), unknown natural voids, raveling, as well as laterally variable soil/rock layering including rock pinnacles. The results are confirmed later by standard penetration tests, including depth to bedrock, two buried voids, and a raveling soil zone. Our study provides insight into the application of the 3D seismic FWI technique as a powerful tool in detecting shallow voids and other localized subsurface features.
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This content will become publicly available on November 1, 2026
Ambient Noise Full Waveform Inversion With Neural Operators
Abstract Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end‐to‐end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint‐state method. State‐of‐the‐art optimization techniques built into PyTorch provide neural operators with greater flexibility to improve the optimization dynamics of full waveform inversion, thereby mitigating cycle‐skipping problems. In this study, we demonstrate the first application of neural operators for full waveform inversion on a real seismic data set, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.
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- Award ID(s):
- 2438772
- PAR ID:
- 10654382
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 130
- Issue:
- 11
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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