三维边缘保持滤波方法在海上地震数据噪声压制中的应用研究——以东海某凹陷为例

doi:10.11720/wtyht.2021.1283

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Abstract

Block C of the East China Sea has the characteristics of complex fault block development,strong stratigraphic heterogeneity,and complex reservoir connectivity.Affected by acquisition and other factors, the random noise of seismic data is relatively serious, which brings great challenges to subsequent exploration and development work.To eliminate random noise effectively and keep the structure information of seismic reflection at the same time,this paper proposes a de-noising method by combining anisotropic diffusion filter technique and F-X edge-preserving filter method.The new method obtains the dip and azimuth of the target formation by gradient structure tensor,extracts the filter window at the target point under the constraints of the dip and azimuth information,and uses adaptive F-X filtering in the filter window to suppress random noise,finally reaching the goals of improving the continuity of seismic reflection in complex fault zone and enhancing the coherence of reflection event and highlighting the edges.The results of 3D modeling and application in real 3D data indicate that the new method has effectively smoothed the reflection events without blurring relevant details of faults and discontinuities on seismic sections. Compared with conventional de-noising method,the new method can improve signal-to-noise ratio and protect seismic reflection and structural information.Meanwhile,the continuity of the edge information of the geological body is better displayed in the time slice.

Key words： structure oriented complex fault block anisotropic diffusion filter F-X edge-preserving

Received: 03 June 2020 Published: 27 July 2021

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	Yong-Zhuang SUN
	Jian LI
	De-Wen QIN
	Qing-Wen LIU

Cite this article:

Yong-Zhuang SUN,Jian LI,De-Wen QIN, et al. The application of 3D edge-preserving de-noising methods based on structure oriented in a complex faults block:A case study of C oilfield in East China Sea[J]. Geophysical and Geochemical Exploration, 2021, 45(3): 692-701.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1283 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I3/692

The geological structure table corresponding to eigenvalues and eigenvectors

Estimate the normal direction v₁ of the local strata according to the structure tensor matrix

Schematic diagram of multi-window filter window
a—hexagonal filter window;b—pentagonal filter window;c—square filter window

Three-dimensional fault model with and without noise
a—raw model without noise;b—the model with noise of 5 dB

Slices of raw model and model with noise
a—raw model inline;b—raw model xline;c—raw model time slice;d—model with noise inline;e—model with noise xline;f—model with noise time slice

De-noising result by conventional F-X method
a—de-noising model inline;b—de-noising model xline;c—de-noising model time slice

De-noising result by anisotropic diffusion filter method
a—de-noising model inline;b—de-noising model xline;c—de-noising model time slice

De-noising result of the method proposed in this paper
a—de-noising model inline;b—de-noising model xline;c—de-noising model time slice

Original data,noisy data and the results of three de-noising methods in single trace comparison
1—raw data;2—de-noising result by our new approach;3—de-noising result by the anisotropic approach;4—noise data;5—de-noising result by traditional F-X approach

Raw seismic data and corresponding structural dip and azimuth profiles
a—raw seismic data;b—structural dip;c—structural azimuth

Profiles of different filtering approaches
a—de-noising result with new filtering approach;b—de-noising result with traditional F-X filtering approach;c—de-noising result with anisotropic diffusion filtering approach

Time slices of different method
a—de-noising time slice with new filtering approach;b—de-noising time slice with traditional F-X filtering approach;c—de-noising time slice with anisotropic diffusion filtering approach

De-noising result before and after using different filter approaches
1—raw data;2—de-noising result by our new approach;3—de-noising result by the anisotropic approach;4—de-noising result by traditional F-X approach

Profiles of de-noising error with different filtering approaches
a—de-noising error with new filtering approach;b—de-noising error with traditional F-X filtering approach;c—de-noising error with anisotropic diffusion filtering approach

Time slices of de-noising error with different filtering approaches
a—time slice of de-noising error with new filtering approach;b—time slice of de-noising error with traditional F-X filtering approach;c—time slice of de-noising error with anisotropic diffusion filtering approach

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