Work

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

Year: 2025

Type: article

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 [Formula: see text] 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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0329793

Authors: Haiqiang Liu, Guangjian Ge, Ze Jiang, Xiaojing Chen

Institutions: China Coal Technology and Engineering Group Corp (China), Jinling Institute of Technology, Nanjing Normal University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,382

Funders: China Coal Technology Engineering Group, Jinling Institute of Technology, Jiangsu Planned Projects for Postdoctoral Research Funds