Research on compressive sensing of strong earthquake signals for earthquake early warning

Abstract

Earthquake early warning is an effective method to reduce casualties and losses. Based on the theory of compressive sensing, this paper proposes a strong earthquake signal processing architecture based on compressive sensing for difficulties of the acquisition, transmission and storage of massive strong earthquake data that the large-scale earthquake warning systems faced. Based on this, the observation vector of strong earthquake signal can be obtained with a predetermined compression ratio and used for transmission and storage. When necessary, the observation value is reconstructed to restore the original strong earthquake signal with a high probability. The paper analyzes the selection of sparse basis and observation matrix, discusses the reconstruction algorithms, base pursuit (BP) and orthogonal matching pursuit (OMP), and verifies the feasibility of the whole framework from compressed sampling to data reconstruction through experiments. The proposed framework has brought great convenience to the sampling, transmission, storage, and processing of the earthquake early warning system. It is foreseeable that replacing the traditional Nyquist sampling value with the observation values in the compressive sensing theory will cause a fundamental change in the signal characteristics, which will further affect the theory and technical system of the entire earthquake early warning system.