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Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it is faced with the limitation of energy resources and the transmission bottleneck of sensors in WSN. In this paper, a Model-Based Adaptive Iterative Hard Thresholding (MAIHT) compressive sensing scheme is developed, where a large number of inexpensive sensors are used to collect fine-grained, real-time volcano-seismic signals while a small number of powerful coordinator nodes process and pick arrival times of primary waves (i.e., P-phases). The paper contribution is two-fold. Firstly, a sparse measurement matrix with theoretical analysis of its restricted isometry property (RIP) is designed to simplify the acquisition process, thereby reducing required storage space and computational demands in sensors. Secondly, a compressive sensing reconstruction algorithm with theoretical analysis of its error bound is presented. Experimental results based on real volcano-seismic data collected from a volcano show that our method can recover the original seismic signal and achieve accurate P-phase picking based on the reconstructed seismic signal.

Model-Based Adaptive Iterative Hard Thresholding Compressive Sensing in Sensor Network for Volcanic Earthquake Detection