An efficient elliptic curve-based deterministic measurement matrix for micro-seismic data acquisition

Abstract

Micro-seismic monitoring plays a critical role in geological disaster prediction. Current compressed sensing approaches for micro-seismic data acquisition, however, are hampered by measurement matrices with high computational complexity and inadequate reconstruction accuracy, particularly in resource-constrained sensor networks. To overcome these limitations, we propose an novel deterministic measurement matrix based on elliptic curve pseudo-random sequences. The proposed matrix offers three key advantages: (1) it requires only M×(N−1) addition operations. (2) it demonstrates lower mutual coherence than existing alternatives. And (3) experimental validation shows consistent performance advantages, achieving lower relative errors than Gaussian random, Chaotic, PEG, and Bernoulli matrices across both noise-free and noisy conditions. These combined improvements in computational efficiency, matrix incoherence, and reconstruction fidelity make our solution ideally suited for real-time micro-seismic monitoring in energy-limited field deployments.