Distributed Traveltime Tomography Using Kernel-Based Regression in Seismic Networks
2022
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.
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