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Two Implementations for τ-p Transforms

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Authors:Zheng Zhisheng; Liu Yang; Liu Cai; Zhang Liang
Author Affiliations:Primary:
Jilin University, College of GeoExploration Science and Technology, Changchun, China
Other:
Qian'an Oil Factory, Jilin Oilfield, PetroChina, China
Volume Title:Jilin Daxue Xuebao (Diqiu Kexue Ban) Journal of Jilin University (Earth Science Edition)
Source:Jilin Daxue Xuebao (Diqiu Kexue Ban) = Journal of Jilin University (Earth Science Edition), 49(6), p.1780-1787. Publisher: Jilin University Press, Changchun, China. ISSN: 1671-5888
Publication Date:2019
Note:In Chinese with English summary
Summary:τ-p transform is a classic projection transform method, which is widely used in seismic data processing such as signal-noise separation, seismic trace interpolation, and plane wave decomposition based on different ray parameters. However, the precision and resolution of τ-p transform are limited by mathematical inverse problems, and it is necessary to study the methods to accelerate the calculation speed of τ-p transform or to design relevant filter, while maintaining high precision. We propose two τ-p transform methods based on radial trace transform and slope decomposition respectively. In this paper, we demonstrate the fundamental theory of the two methods and compare them with conventional τ-p transform. Using fast Fourier transform and radial trace transform, the τ-p transform method can efficiently reduce the time consumption. Utilizing stable slope decomposition algorithm, the τ-p transform method can perform high resolution τ-p transform, and provide redundant transformation dimension(τ-x-p domain), which enable the design of filter operator more flexible. The numerical experiments show that these two proposed methods are superior to the conventional τ-p transform in calculation speed and reconstruction accuracy. The field-data examples for ground-roll noise suppression confirm that the two methods can provide more effective and flexible implementations for seismic data processing.
Record ID:874306-24
Copyright Information:GeoRef, Copyright 2021 American Geosciences Institute.
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