DOA Estimation in Wireless Seismic Surveys Using Deep Learning

Almehdhar, Ahmed; Hamida, Amira Ben; Aliyu, Kabiru N.; Alawsh, Saleh A.; Muqaibel, Ali H.; Al-Dharrab, Suhail; Mesbah, Wessam; Stuber, G.L.

doi:10.1109/iccspa49915.2021.9385716

Cited by 2 publications

(1 citation statement)

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“…Given two or more estimated DOAs with coordinates of the receiver, the location of the transmitter can be estimated using a least square estimation (LSE) [29], [30]. In [31], DOA estimation based DNN is proposed for wireless seismic survey. In this paper, DNN is proposed for DOA estimation of WGs at the WGN under different channel conditions.…”

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confidence: 99%

DOA-based Localization Using Deep Learning for Wireless Seismic Acquisition

Aliyu

Alawsh

Muqaibel

et al. 2021

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Oil and gas companies consider transforming conventional cable-based seismic acquisition to wireless acquisition as a promising step for cost and weight reduction in reservoir exploration. Wireless seismic acquisition requires large number of wireless geophone (WG) sensors to be deployed in the field. The locations of the WG sensors must be known when processing the collected data. The application of direction of arrival (DOA) estimation helps in localizing WGs and improves received signal level through beam steering and interference avoidance. Conventional DOA algorithms require high computational complexity which renders them inefficient for real-time response. In this paper, deep neural network (DNN) is proposed for DOA estimation of WGs at wireless gateway node (WGN) under different channel conditions. The estimated angle and corresponding coordinates of WGNs are used in least square estimation (LSE) to estimate the position of the WGs. The simulation results depict reasonable estimation and position accuracy in real-time.

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confidence: 99%