FISH Lecture - Fangyu Li (Kennesaw State University)
Title: Distributed Sensor Networks based Real-Time and In-Situ Subsurface Imaging
Near-surface geophysical imaging is critical to explore resource plays and characterize the subsurface sustainability. However, conventional subsurface imaging requires a long-term processing, including data acquisition, collection, processing, imaging, QC procedures and so on, which could take months. Distributed sensor networks (DSN) based Real-time and In-situ Subsurface Imaging (RISI) boosts up the near-surface characterization efficiency, which is essential to timely assess earth resources and potential hazards of geological structures. Dr. Fangyu Li has been actively working on creating a DSN based RISI system to study and monitor the near surface. The system is comprised of a self-sustainable sensor network of geophones that can autonomously perform in-network computing of the 3D shallow earth structure images in-situ and in real time. The proposed approach is general and can be implemented as a monitoring system in highly dynamic and complex environments for both natural and human activities, such as volcanoes, microearthquakes, underground infrastructure and so on.
Dr. Fangyu Li is an assistant professor with the Department of Electrical and Computer Engineering at Kennesaw State University (KSU). Before joining KSU, he was a postdoctoral fellow with the College of Engineering, University of Georgia. He received his PhD in Geophysics from University of Oklahoma in 2017. His Master (2013) and Bachelor (2009) degrees were obtained from Tsinghua University and Beihang University, respectively. His research interests include seismic signal processing, subsurface imaging, quantitative interpretation, machine learning, distributed computing, Internet of things (IoT), and cyber-physical systems (CPS). Dr. Li is the recipient of the 2020 J. Clarence Karcher Award from the Society of Exploration Geophysicists (SEG).