Distributed tomography with adaptive mesh refinement in sensor networks

Existing seismic instrumentation systems do not yet have the capability to recover the physical dynamics with sufficient resolution in real time. Currently, seismologists use centralized tomography inversion algorithm for which the data is gathered either manually from each station or by using limited number of expensive broadband stations. This scheme can take months to generate tomography and also lack the resolution due to limited number of sensors. It also introduces a bottleneck in computation and increases the risk of data loss in case of node failures, especially the base station. To address these issues a distributed approach is required which can avoid costly data collection from large number of sensors and perform in-network imaging to obtain high resolution real-time tomography. In this paper, we present a distributed adaptive mesh refinement solution to invert seismic tomography over large dense network, which avoids centralized computation and expensive data collection. Our approach first discretizes the high fidility data and later filters them using adaptive mesh to make it well-conditioned. We show that this filtered well conditioned system has lower dimension and improved convergence rate than the original system, thereby decreasing the communication overhead over the network. The system is implemented and evaluated using a CORE emulator and the results show that our method is able to obtain high-resolution images in real-time by distributing the computation load over the network.