An efficient elliptic curve-based deterministic measurement matrix for micro-seismic data acquisition
Haiqiang Liu,
Guangjian Ge,
Ze Jiang and
Xiaojing Chen
PLOS ONE, 2025, vol. 20, issue 8, 1-14
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.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0329793 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 29793&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0329793
DOI: 10.1371/journal.pone.0329793
Access Statistics for this article
More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().