Sensor Network for Real-time In-situ Seismic Tomography

Lei Shi, Wen-Zhan Song, Fan Dong, Goutham Kamath

Abstract

Most existing seismic exploration or volcano monitoring systems employ expensive broadband seismometer as instrumentation. At present raw seismic data are typically collected at central observatories for post processing. With a high-fidelity sampling, it is virtually impossible to collect raw, real-time data from a large-scale dense sensor network due to severe limitations of energy and bandwidth at current, battery-powered sensor nodes. At some most threatening and active volcanoes, only tens of nodes are maintained. With a small network and post processing mechanism, existing system do not yet have the capability to recover physical dynamics with sufficient resolution in real-time. This limits our ability to understand earthquake zone or volcano dynamics. To obtain the seismic tomography in real-time and high resolution, a new sensor network system for real-time in-situ seismic tomography computation is proposed in this paper. The design of the sensor network consists of hardware, sensing and data processing components for automatic arrivaltime picking and tomography computation. This system design is evaluated both in lab environment for 3D tomography with real seismic data set and in outdoor field test for 2D surface tomography.

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Paper Citation


in Harvard Style

Shi L., Song W., Dong F. and Kamath G. (2016). Sensor Network for Real-time In-situ Seismic Tomography . In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD, ISBN 978-989-758-183-0, pages 118-128. DOI: 10.5220/0005897501180128


in Bibtex Style

@conference{iotbd16,
author={Lei Shi and Wen-Zhan Song and Fan Dong and Goutham Kamath},
title={Sensor Network for Real-time In-situ Seismic Tomography},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,},
year={2016},
pages={118-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005897501180128},
isbn={978-989-758-183-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,
TI - Sensor Network for Real-time In-situ Seismic Tomography
SN - 978-989-758-183-0
AU - Shi L.
AU - Song W.
AU - Dong F.
AU - Kamath G.
PY - 2016
SP - 118
EP - 128
DO - 10.5220/0005897501180128