Distributed Traveltime Tomography Using Kernel-Based Regression in Seismic Networks

Distributed subsurface imaging is of high relevance for autonomous seismic surveys by multiagent networks as envisioned for future planetary missions. The goal is to achieve a cooperative reconstruction of a subsurface image at each agent by relying on data exchange among the agents. To this end, distributed full waveform inversion (FWI) for high-resolution imaging has been proposed. However, FWI always requires an initial model of the subsurface. To provide each agent in the network with such a model, we propose a distributed traveltime tomography (TT). To this end, we integrate a distributed kernel-based regression of traveltime residuals into TT. By that, each agent computes an approximation of all time residuals in the network and can perform a TT to obtain a subsurface image locally. We conduct numerical evaluations for a synthetic subsurface model and the SEG salt model. The results show that each receiver indeed achieves a subsurface image that is close to the global result even for a low network connectivity.