Distributed tomography with adaptive mesh refinement in sensor networks

Abstract

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.Keywords: distributed sensing; adaptive mesh; seismic tomography; sensor network; in-network computing.Reference to this paper should be made as follows: Kamath, G., Shi, L., Chow, E. and Song, W-Z. Copyright © 2017 Inderscience Enterprises Ltd. Distributed tomography with adaptive mesh refinement in sensor networks 41Wen-Zhan Song is a Professor in Georgia State University. His research mainly focuses on sensor web, smart grid and smart environment where sensing, computing, communication and control play a critical role and need a transformative study. His research has received 6 million+ research funding from NSF, NASA, USGS, Boeing and etc since 2005, and resulted in 80+ journal papers, conference papers and book chapters in this area. This paper is a revised and expanded version of a paper entitled 'Component-average based distributed seismic tomography in sensor networks' presented at

Toward Creating a Subsurface Camera

“…Thus the main research challenge is to develop distributed iterative computing algorithms under network bandwidth constraints. Song et al [17][18][19][20][21][22][23][24][25][26][27] pioneered the research on in-situ seismic imaging in distributed sensor networks. The idea is to let each node compute in an asynchronous fashion and communicate with neighbors only while solving 2D/3D image reconstruction problem.…”

“…In the harsh geological field environment, the network disruptions are not unusual and reliable multi-hop communication is not easy to achieve. Prototype system based on TomoTT [17][18][19][20][21][23][24][25][26][27] was designed and demonstrated. In 27 , a distributed computing algorithm based on vertical partition was proposed.…”

Distributed and Communication-Efficient Spatial Auto-Correlation Subsurface Imaging in Sensor Networks

“…We deployed smart seismic sensor nodes at the University of Georgia to generate the velocity map and 3D structure of the subsurface using our dSPAC system and our optimized communication-reduced model. The previous systems demonstrated promising potential in illuminating either deep or shallow subsurface depending on the tomography method used within them [10,29,30]. However, one key problem of these systems is validation.…”