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Abstract
The use of seismic arrays as a tool for imaging subsurface infrastructures and monitoring the corresponding underground activities enables real-time subsurface security and surveillance applications. However, the existing approaches rely on manual data collection and/or centralized computing, which is not scalable as network size grows, and bottleneck problems can occurr when all data is sending to a central point. These approaches may also take a long time to get useful results. In this dissertation, we investigate and propose an in-situ and cooperative subsurface imaging system for a variety of subsurface infrastructure imaging applications. The proposed approach integrates in-situ signal processing techniques as well as inter-nodes communication and cooperation to obtain reliable velocity maps for subsurface characterization and monitoring. It generates real-time subsurface images by taking advantage of collective computation power in sensor networks while avoiding transferring all raw data to a central place or server. Eikonal tomography and Spatial Autocorrelation methodologies are studied to solve imaging challenges. The system is autonomous, self-healing, scalable and almost independent of external interventions. A communication-reduced method is investigated to meet bandwidth, communication, and energy constraints of the sensors during the imaging process. Experiments are conducted in both, emulator and field scenarios. An exhaustive evaluation regarding bandwidth utilization and communication cost were conducted to highlight the benefits of the proposed approach. The uses can be extended to other applications like border security, building monitoring, underground water detection for agriculture, and more.