Atom-based-segmented MP compression algorithm for seismic data

• Published in: IET signal processing Vol. 12; no. 3; pp. 284 - 293
• Main Authors: Yin, Zhiyuan, Zhou, Yan, Li, Yongxin
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.05.2018
• Subjects: atom‐based‐segmented MP compression algorithm; data compression; data quantity; fidelity requirement; geophysical signal processing; high‐fidelity high‐compression ratio; low‐compression time; matching pursuit; modified Morlet wave atom dictionary; Research Article; sampling time; seismic data transmission efficiency; seismic exploration; seismology; site‐collected seismic data compression; data compression; seismic exploration; modified Morlet wave atom dictionary; site-collected seismic data compression; seismology; data quantity; geophysical signal processing; matching pursuit; seismic data transmission efficiency; low-compression time; sampling time; atom-based-segmented MP compression algorithm; high-fidelity high-compression ratio; fidelity requirement
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2017.0226

Data compression is an effective way to improve the seismic data transmission efficiency. The features of seismic exploration are long sampling time and large data quantity, so the compression algorithm should achieve high-fidelity, high-compression ratio (CR) and low-compression time. Since the existing compression algorithms cannot meet the requirements of site-collected seismic data compression, the segmented matching pursuit (SMP) compression algorithm based on new atom dictionary is proposed. This method is based on the principle of MP. A novel-segmented compression structure is adopted. The modified Morlet wave atom dictionary is designed to replace the previous dictionaries. The results of comparative experiments show that the proposed algorithm makes improvements in CR and compression time with the same fidelity requirement.