Distributed Subsurface Imaging with Tikhonov and Total Variation Regularization for Seismic Networks

Future space exploration missions envision multiagent networks autonomously conducting subsurface exploration. Achieving this requires distributed subsurface imaging. Previously, we introduced the adapt-then-combine full waveform inversion (ATCFWI) in both the time and frequency domains, enabling each receiver in a seismic network to generate high-resolution subsurface images locally through data exchange with neighboring receivers. To further enhance imaging quality, we explore the regularization of local cost functions. Specifically, we incorporate Tikhonov and total variation (TV) regularization. Tikhonov regularization promotes smooth structural recovery, while TV regularization enhances edge sharpness and contrast. We integrate both methods into ATCFWI to enable robust imaging in a distributed fashion. Through numerical evaluations, we demonstrate that ATCFWI, combined with these regularization techniques, significantly improves imaging performance across all receivers. Code for our proposed method is available on https://github.com/bshin/fd-atcfwi.