Distributed tomography with adaptive mesh refinement in sensor networks

摘要

Existing seismic instrumentation systems do not yet have the capability to recover the physical dynamics with sufficient resolution in real time. Currently, seismologists use centralised tomography inversion algorithm, which requires manual data gathering from each station and months to generate tomography. To address these issues a distributed approach is required which can avoid data collection from large number of sensors and perform in-network imaging to real-time tomography. In this paper, we present a distributed adaptive mesh refinement (AMR) solution to invert seismic tomography over large dense network, which avoids centralised computation and expensive data collection. Our approach first discretises the data and filters them using adaptive mesh to make it well-conditioned. The system is implemented and evaluated using a CORE emulator and we show that the filtered well-conditioned system has lower dimension and improved convergence rate than the original system, thereby decreasing the communication overhead over the network.