Distributed Blind Deconvolution of Seismic Signals under Sparsity Constraints in Sensor Networks

This work proposes an iterative algorithm for distributed deconvolution of seismic signals for a reflectivity survey by a network of sensors. Distributed deconvolution is particularly relevant for a subsurface exploration by sensor networks or swarms of mobile robots. We envision such an exploration methodology by multiple mobile agents for future explorations of a planet's subsurface. The proposed scheme consists of two steps: distributed estimation of the seismic wavelet, followed by a local estimation of the reflectivity. Both steps are realized using alternating directions method of multipliers algorithms where we exploit sparsity in the reflectivity. The performance of the scheme is compared to state-of-the-art sparse multichannel blind deconvolution of seismic data and is found to be comparable or even superior.