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

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.