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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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Use of Elastic Full Waveform Inversion for Monitoring of Dams and Levees
Understanding subsurface conditions is critical to creating and maintaining resilient infrastructure systems, such as dams and levees. Seismic geophysical tools can be very effective for site characterization of these structures as they directly measure the elastic moduli and can provide insight into both the soil properties and groundwater conditions. Full waveform inversion (FWI) is one processing option for seismic geophysics that seeks to overcome some of the limitations in the traditional approaches by using the full time-domain recording of the wavefield to develop 2D or 3D profiles of shear wave velocity. In addition to providing characterization data, FWI can also potentially be used as a monitoring tool for dams and levees to assess how elastic moduli are changing with time and to infer how these changes might relate to changes in the hydromechanical properties of the soil. This study seeks to explore the use of seismic FWI as both a characterization and monitoring tool through numerical simulations of seismic surveys on a hypothetical levee with a low velocity anomaly in the foundation. The simulations are used to assess both the spatial resolution and the ability of the simulations to detect changes in properties that might be related to softening of the foundation or development of internal erosion failure modes. The findings from the study will be used to highlight potential benefits and challenges to using seismic FWI for characterization and monitoring of dams and levees.
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- Award ID(s):
- 2047402
- PAR ID:
- 10519340
- Publisher / Repository:
- Proceedings of the 7th International Conference on Geotechnical and Geophysical Site Characterization
- Date Published:
- Format(s):
- Medium: X
- Location:
- Barcelona, Spain
- Sponsoring Org:
- National Science Foundation
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