DOA Estimation in Wireless Seismic Surveys Using Deep Learning

摘要

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.