Atom‐based‐segmented MP compression algorithm for seismic data. Issue 3 (1st May 2018)

• Record Type: Journal Article
• Authors: Yin, Zhiyuan; Zhou, Yan; Li, Yongxin
• Is Part Of: IET signal processing. Volume 12:Issue 3(2018)
• Page Start: 284
• Page End: 293
• Publication Date: 2018-05-01
• Subjects: data compression -- seismology -- geophysical signal processing; atom‐based‐segmented MP compression algorithm -- data compression -- seismic data transmission efficiency -- seismic exploration -- sampling time -- data quantity -- high‐fidelity high‐compression ratio -- low‐compression time -- site‐collected seismic data compression -- matching pursuit -- modified Morlet wave atom dictionary -- fidelity requirement; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2017.0226
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : 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.