Real-Time Data Sensing for Microseismic Monitoring via Adaptive Compressed Sampling

The large amount of monitoring data has posed enormous challenges to the quick response and accurate analysis of microseismic events. Compressed sensing (CS) has the advantages of low resource cost, high efficiency, and excellent data compression ratio (CR), over conventional sensing methods. However, there are still issues to be addressed for its applications: 1) the poor quality and complex signal structure significantly increased the difficulty of keeping satisfactory efficiency; 2) the systematic design of the sparse dictionary, and the measurement matrix for microseismic signal CS are still poor; and 3) the conventional recovery algorithms also require prior knowledge of signal sparsity, which is hardly possible to know or estimate in practice. Therefore, an adaptive real-time sensing method for microseismic monitoring from the perspective of systematic design was proposed in this work. We first analyzed noise and signal structure characteristics to construct an over-complete learning dictionary. Second, according to the learned dictionary, we analyzed the key performance factors of random projection through comparison between different matrices. Third, we explored the relationship between the signal sparsity and the residual energy decay during data recovery with the greedy pursuit algorithms and then presented an energy-ratio-based sparsity adaptive matching algorithm. Finally, we carried out the performance evaluation of the proposed real-time sensing method through synthetic signals and field monitoring data.

Automated summary

This study proposed an adaptive real-time sensing method for microseismic monitoring from the perspective of systematic design. It first constructed an over-complete learning dictionary by analyzing noise and signal structure characteristics. Then, it analyzed the key performance factors of random projection through comparison between different matrices according to the learned dictionary. It also explored the relationship between signal sparsity and residual energy decay during data recovery with greedy pursuit algorithms and presented an energy-ratio-based sparsity adaptive matching algorithm. Finally, the performance evaluation of the proposed method was conducted using synthetic signals and field monitoring data.