Wavenumber-aware diffusion sampling to regularize multiparameter elastic full waveform inversion

SUMMARY Multiparameter elastic full waveform inversion (EFWI) provides a more realistic depiction of the subsurface models than the standard acoustic approximation. In practice, however, the significant additional cost and interdependence between the unknown parameters (cross-talks) hinder the application of such algorithms. Diffusion model-based regularization can be used to improve the inversion results while simultaneously injecting prior information into the solution. The main challenge here is how to inject such priors into the EFWI iterations that can better complement the solution’s evolution. To address this challenge, we incorporate a model wavenumber continuation process into a diffusion model-based regularization contribution to multiparameter EFWI. To do so, we promote a sampling strategy such that at the early iteration, the proposed regularization updates account for the low wavenumber component more and increase progressively with the iteration. We first train the diffusion model on elastic moduli images in an unsupervised manner and incorporate the trained model during the EFWI inversion. We deliberately use single-component measurements, which is the most common acquisition scenario, during the inversion to demonstrate the effectiveness of our regularization. At the inference stage, the proposed framework provides more accurate solutions with negligible additional computational cost compared to several conventional regularization algorithms.