Distributed Traveltime Tomography Using Kernel-Based Regression in Seismic Networks

Abstract

Distributed subsurface imaging is of high relevance for autonomous seismic surveys by multi-agent 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 for high-resolution imaging has been proposed. However, full waveform inversion 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. To this end, we integrate a distributed kernel-based regression of traveltime residuals into traveltime tomography. By that, each agent computes an approximation of all time residuals in the network and can perform a traveltime tomography 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.