DOA Estimation in Wireless Seismic Surveys Using Deep Learning

A. Almehdhar; A. Hamida; K. Aliyu; S. Alawsh; A. Muqaibel; S. Al-Dharrab; W. Mesbah; Gordon L. Stüber

doi:10.1109/ICCSPA49915.2021.9385716

DOA Estimation in Wireless Seismic Surveys Using Deep Learning

A. Almehdhar, A. Hamida, K. Aliyu, S. Alawsh, A. Muqaibel, S. Al-Dharrab, W. Mesbah, Gordon L. Stüber

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

Abstract

Current seismic survey systems use wired telemetry to collect seismic data from sensors (geophones), and due to the massive cabling requirement, the current wired systems are limited by weight and cost. Replacing current systems with wireless technologies is becoming a more practical and economical choice. Once sensors are wireless, localizing them becomes a necessity when interpreting seismic data. Direction of Arrival (DOA) estimation can be used for source localization. In this paper, DOA estimation based on Deep Neural Network (DNN) is proposed for wireless seismic survey. In terms of accuracy in estimation, simulation results are promising.

Original language	English
Title of host publication	ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728165356
DOIs	https://doi.org/10.1109/ICCSPA49915.2021.9385716
State	Published - 16 Mar 2021

Publication series

Name	ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications
Volume	2021-January

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Deep Neural Network
Direction of Arrival
Geophone
Wireless Seismic Surveys

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICCSPA49915.2021.9385716

Cite this

Almehdhar, A., Hamida, A., Aliyu, K., Alawsh, S., Muqaibel, A., Al-Dharrab, S., Mesbah, W., & Stüber, G. L. (2021). DOA Estimation in Wireless Seismic Surveys Using Deep Learning. In ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications (ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications; Vol. 2021-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCSPA49915.2021.9385716

Almehdhar, A. ; Hamida, A. ; Aliyu, K. et al. / DOA Estimation in Wireless Seismic Surveys Using Deep Learning. ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications. Institute of Electrical and Electronics Engineers Inc., 2021. (ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications).

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title = "DOA Estimation in Wireless Seismic Surveys Using Deep Learning",

abstract = "Current seismic survey systems use wired telemetry to collect seismic data from sensors (geophones), and due to the massive cabling requirement, the current wired systems are limited by weight and cost. Replacing current systems with wireless technologies is becoming a more practical and economical choice. Once sensors are wireless, localizing them becomes a necessity when interpreting seismic data. Direction of Arrival (DOA) estimation can be used for source localization. In this paper, DOA estimation based on Deep Neural Network (DNN) is proposed for wireless seismic survey. In terms of accuracy in estimation, simulation results are promising.",

keywords = "Deep Neural Network, Direction of Arrival, Geophone, Wireless Seismic Surveys",

author = "A. Almehdhar and A. Hamida and K. Aliyu and S. Alawsh and A. Muqaibel and S. Al-Dharrab and W. Mesbah and St{\"u}ber, {Gordon L.}",

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doi = "10.1109/ICCSPA49915.2021.9385716",

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Almehdhar, A, Hamida, A, Aliyu, K, Alawsh, S , Muqaibel, A , Al-Dharrab, S , Mesbah, W & Stüber, GL 2021, DOA Estimation in Wireless Seismic Surveys Using Deep Learning. in ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications. ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications, vol. 2021-January, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICCSPA49915.2021.9385716

DOA Estimation in Wireless Seismic Surveys Using Deep Learning. / Almehdhar, A.; Hamida, A.; Aliyu, K. et al.
ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications. Institute of Electrical and Electronics Engineers Inc., 2021. (ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications; Vol. 2021-January).

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

TY - GEN

T1 - DOA Estimation in Wireless Seismic Surveys Using Deep Learning

AU - Almehdhar, A.

AU - Hamida, A.

AU - Aliyu, K.

AU - Alawsh, S.

AU - Muqaibel, A.

AU - Al-Dharrab, S.

AU - Mesbah, W.

AU - Stüber, Gordon L.

PY - 2021/3/16

Y1 - 2021/3/16

N2 - Current seismic survey systems use wired telemetry to collect seismic data from sensors (geophones), and due to the massive cabling requirement, the current wired systems are limited by weight and cost. Replacing current systems with wireless technologies is becoming a more practical and economical choice. Once sensors are wireless, localizing them becomes a necessity when interpreting seismic data. Direction of Arrival (DOA) estimation can be used for source localization. In this paper, DOA estimation based on Deep Neural Network (DNN) is proposed for wireless seismic survey. In terms of accuracy in estimation, simulation results are promising.

AB - Current seismic survey systems use wired telemetry to collect seismic data from sensors (geophones), and due to the massive cabling requirement, the current wired systems are limited by weight and cost. Replacing current systems with wireless technologies is becoming a more practical and economical choice. Once sensors are wireless, localizing them becomes a necessity when interpreting seismic data. Direction of Arrival (DOA) estimation can be used for source localization. In this paper, DOA estimation based on Deep Neural Network (DNN) is proposed for wireless seismic survey. In terms of accuracy in estimation, simulation results are promising.

KW - Deep Neural Network

KW - Direction of Arrival

KW - Geophone

KW - Wireless Seismic Surveys

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U2 - 10.1109/ICCSPA49915.2021.9385716

DO - 10.1109/ICCSPA49915.2021.9385716

M3 - Conference contribution

AN - SCOPUS:85125014943

T3 - ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications

BT - ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Almehdhar A, Hamida A, Aliyu K, Alawsh S , Muqaibel A , Al-Dharrab S et al. DOA Estimation in Wireless Seismic Surveys Using Deep Learning. In ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications. Institute of Electrical and Electronics Engineers Inc. 2021. (ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications). doi: 10.1109/ICCSPA49915.2021.9385716

DOA Estimation in Wireless Seismic Surveys Using Deep Learning

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this