Abstract
Seismic data collection involves vast amounts of data in the range of multiple hundreds of terabytes. Very high data compression algorithms are thus required to make seismic data analysis more efficient in terms of storage and transmission bandwidth. We present an adaptive filtering algorithm for seismic data compression based on the wavelet packet transform. The main objective is to achieve higher compression rates for seismic data at a quality above 40 dB in the SNR perspective. The wavelet transform is known for its good localization in time and frequency, a feature that has been utilized for data compression. Wavelet packets are introduced to give extra freedom in selecting appropriate position, scale. and frequency decomposition for more data compression. Our algorithm is developed so that the selection of the best tree decomposition of any given signal is adaptively obtained with respect to an entropy-based criterion. Our algorithm is tested on different seismic data sets. Results indicate that the proposed algorithm is more efficient when compared to other algorithms in the same category without showing any visible artifacts in the decompressed signals.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
