Adapt-Then-Combine Full Waveform Inversion For Distributed Subsurface Imaging In Seismic Networks

We consider the problem of distributed subsurface imaging in seismic receiver networks. This problem is particularly relevant for future planetary exploration missions where multi-agent networks shall autonomously reconstruct a subsurface based on network-wide measurements. To this end, we propose a distributed implementation of the full waveform inversion (FWI) for distributed imaging of subsurfaces in seismic networks. In particular, we show that the gradient of FWI is equivalent to the sum of locally computed gradients. To obtain estimates of the global gradient and subsurface model at each receiver we employ the adapt-then-combine technique that relies on data exchange among neighboring receivers only. Numerical evaluations show that the proposed distributed FWI performs close to its centralized version for different source-receiver constellations.