Passive Seismic Monitoring Using DAS and Deep Learning Models

Abstract

Passive seismic monitoring is valuable for studying the Earth's subsurface and identifying seismic activity that poses hazards. Significant technological advancements in passive seismic monitoring have led to networks with an increasing number of recording channels, necessitating timely transmission and processing for fast detection and reporting. An emerging technology for passive seismic monitoring is distributed acoustic sensing (DAS). The substantial amount of data produced by DAS presents a challenge, requiring the development of new technologies for its efficient handling and processing. This talk introduces deep learning-based methodologies for processing DAS data to ensure efficient transmission and visualization.

Bio

Naveed Iqbal received his B.S. and M.S. degrees in electrical engineering from the University of Engineering and Technology, Peshawar, Pakistan, and Ph.D. degree from King Fahd University of Petroleum and Minerals, Saudi Arabia. He is currently an Assistant Professor at King Fahd University of Petroleum and Minerals. His research interests include adaptive algorithms, compressive sensing, heuristic algorithms, wireless communication, signal processing, seismic, machine learning, and data acquisition networks.