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.

摘要

地震预警是减少人员伤亡的有效方法。基于压缩感知理论，针对大规模地震预警系统面临的海量强地震数据的采集，传输和存储的难点，提出了一种基于压缩感知的强地震信号处理架构。基于此，可以以预定的压缩率获得强地震信号的观测矢量，并将其用于传输和存储。必要时，重建观测值以高概率恢复原始的强地震信号。本文分析了稀疏基础和观测矩阵的选择，讨论了重建算法，基本追踪（BP）和正交匹配追踪（OMP），并通过实验验证了整个框架从压缩采样到数据重建的可行性。所提出的框架为地震预警系统的采样，传输，存储和处理带来了极大的便利。可以预见，用压缩感测理论中的观测值代替传统的奈奎斯特采样值将引起信号特性的根本变化，这将进一步影响整个地震预警系统的理论和技术体系。