Communication Efficient Signal Detection for Distributed Ambient Noise Imaging

Liyan Xie; Yao Xie; Sin Mei Wu; Fan Chi Lin; Wenzhan Song

doi:10.1109/ACSSC.2018.8645222

Communication Efficient Signal Detection for Distributed Ambient Noise Imaging

Liyan Xie
, Yao Xie
, Sin Mei Wu
, Fan Chi Lin
, Wenzhan Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

We present a signal detection scheme with low-communication overhead for the recently developed distributed ambient noise imaging (ANSI) system for real-time imaging the geophysical subsurface structure. Signal detection enables ANSI to perform adaptive data acquisition. Our method only requires each sensor to exchange a limited number of frequency samples with each other. Then each sensor directly estimates the so-called local temporal energy (LTE) using their frequency samples and received samples from another sensor, without having to form the temporal cross-correlation function. Thus, we avoid transmission of the temporal waveforms, which significantly reduce the communication overhead. Numerical examples on a real seismic dataset demonstrate the good performance of our method.

Original language	English (US)
Title of host publication	Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1779-1783
Number of pages	5
ISBN (Electronic)	9781538692189
DOIs	https://doi.org/10.1109/ACSSC.2018.8645222
State	Published - Jul 2 2018
Externally published	Yes
Event	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: Oct 28 2018 → Oct 31 2018

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2018-October
ISSN (Electronic)	2576-2303

Conference

Conference	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/Territory	United States
City	Pacific Grove
Period	10/28/18 → 10/31/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Publisher link

10.1109/ACSSC.2018.8645222

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Xie, L., Xie, Y., Wu, S. M., Lin, F. C., & Song, W. (2018). Communication Efficient Signal Detection for Distributed Ambient Noise Imaging. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 1779-1783). Article 8645222 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645222

Communication Efficient Signal Detection for Distributed Ambient Noise Imaging. / Xie, Liyan; Xie, Yao; Wu, Sin Mei et al.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2018. p. 1779-1783 8645222 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xie, L, Xie, Y, Wu, SM, Lin, FC & Song, W 2018, Communication Efficient Signal Detection for Distributed Ambient Noise Imaging. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645222, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 1779-1783, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 10/28/18. https://doi.org/10.1109/ACSSC.2018.8645222

Xie L, Xie Y, Wu SM, Lin FC, Song W. Communication Efficient Signal Detection for Distributed Ambient Noise Imaging. In Matthews MB, editor, Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. IEEE Computer Society. 2018. p. 1779-1783. 8645222. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2018.8645222

Xie, Liyan ; Xie, Yao ; Wu, Sin Mei et al. / Communication Efficient Signal Detection for Distributed Ambient Noise Imaging. Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. editor / Michael B. Matthews. IEEE Computer Society, 2018. pp. 1779-1783 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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title = "Communication Efficient Signal Detection for Distributed Ambient Noise Imaging",

abstract = "We present a signal detection scheme with low-communication overhead for the recently developed distributed ambient noise imaging (ANSI) system for real-time imaging the geophysical subsurface structure. Signal detection enables ANSI to perform adaptive data acquisition. Our method only requires each sensor to exchange a limited number of frequency samples with each other. Then each sensor directly estimates the so-called local temporal energy (LTE) using their frequency samples and received samples from another sensor, without having to form the temporal cross-correlation function. Thus, we avoid transmission of the temporal waveforms, which significantly reduce the communication overhead. Numerical examples on a real seismic dataset demonstrate the good performance of our method.",

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AB - We present a signal detection scheme with low-communication overhead for the recently developed distributed ambient noise imaging (ANSI) system for real-time imaging the geophysical subsurface structure. Signal detection enables ANSI to perform adaptive data acquisition. Our method only requires each sensor to exchange a limited number of frequency samples with each other. Then each sensor directly estimates the so-called local temporal energy (LTE) using their frequency samples and received samples from another sensor, without having to form the temporal cross-correlation function. Thus, we avoid transmission of the temporal waveforms, which significantly reduce the communication overhead. Numerical examples on a real seismic dataset demonstrate the good performance of our method.

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Communication Efficient Signal Detection for Distributed Ambient Noise Imaging

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