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SUMMARY Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change near-surface (<30 m) site characterization by enabling the recovery of high-resolution (metre-scale) 2-D/3-D maps of subsurface elastic material properties. Yet, FWI results are quite sensitive to their starting model due to their dependence on local-search optimization techniques and inversion non-uniqueness. Starting model dependence is particularly problematic for near-surface FWI due to the complexity of the recorded seismic wavefield (e.g. dominant surface waves intermixed with body waves) and the potential for significant spatial variability over short distances. In response, convolutional neural networks (CNNs) are investigated as a potential tool for developing starting models for near-surface 2-D elastic FWI. Specifically, 100 000 subsurface models were generated to be representative of a classic near-surface geophysics problem; namely, imaging a two-layer, undulating, soil-over-bedrock interface. A CNN has been developed from these synthetic models that is capable of transforming an experimental wavefield acquired using a seismic source located at the centre of a linear array of 24 closely spaced surface sensors directly into a robust starting model for FWI. The CNN approach was able to produce 2-D starting models with seismic image misfits that were significantly less than the misfits from other common starting model approaches, and in many cases even less than the misfits obtained by FWI with inferior starting models. The ability of the CNN to generalize outside its two-layered training set was assessed using a more complex, three-layered, soil-over-bedrock formation. While the predictive ability of the CNN was slightly reduced for this more complex case, it was still able to achieve seismic image and waveform misfits that were comparable to other commonly used starting models, despite not being trained on any three-layered models. As such, CNNs show great potential as tools for rapidly developing robust, site-specific starting models for near-surface elastic FWI.
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This content will become publicly available on May 1, 2026
On the importance of horizontal components in source-encoded elastic full-waveform inversion: Multicomponent ocean-bottom-node data
Elastic full-waveform inversion (EFWI) is a state-of-the-art seismic tomographic method. Recent advances in technology and instrumentation, combining crosstalk-free source-encoded FWI (SE-FWI) with multicomponent marine data acquisition using ocean-bottom nodes (OBNs), enable full-physics wave propagation and parameter inversion without the computational burden of traditional FWI. With OBN acquisition, P waves, S waves, and P-to-S conversions are recorded. It is not well understood to what extent adding horizontal components to SE-FWI improves the resolution of subsurface modeling. We assess their potential for the reconstruction of shear and compressional wave speeds (VPand VS) by using a synthetic data set modeled after a recently acquired OBN survey in the North Sea. We perform synthetic inversion tests to design suitable strategies that leverage the information recorded in the horizontal components of the data to improve the reconstructed model resolution laterally and in depth. We advocate for a hierarchical inversion approach to recover the elastic parameters. We exploit the P and P-to-S converted waves recorded on the horizontal components to robustly reconstruct both VPand VS. Adding horizontal components to the SE-FWI modeling workflow results in improved spatial resolution, enhanced depth coverage, and more accurate elastic wave speed estimates.
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- Award ID(s):
- 2320649
- PAR ID:
- 10636745
- Publisher / Repository:
- Society of Exploration Geophysicists
- Date Published:
- Journal Name:
- The Leading Edge
- Volume:
- 44
- Issue:
- 5
- ISSN:
- 1070-485X
- Page Range / eLocation ID:
- 417a1 to 417a7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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