Anisotropic diffusion filtering based on fault confidence measure and stratigraphic coherence coefficients

Abstract

We develop an algorithm for seismic data filtering to preserve the boundary information of faults and other geological bodies while suppressing the noise. Based on the theory of anisotropic diffusion filtering, this method explores the relationship between eigenvalues of the structure tensor and local structural features of three‐dimensional seismic images and innovatively introduces the definition of stratigraphic coherence coefficients. Then we propose a new system to design the eigenvalues of the diffusion tensor by using the fault confidence measure and stratigraphic coherence coefficients, which can control the filtering intensity of seismic data in different orientations. The value of fault confidence measure is close to 0 where there are flat continuous reflectors and the intensity of diffusion is strong. On the contrary, the diffusion is very weak within presumptive fault zones. Results of both synthetic model and real data prove that the proposed method can effectively suppress noise, preserve faults well and enhance the continuity of the reflectors, which can provide basic data with high signal‐to‐noise ratio for subsequent seismic interpretation.

Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis

“…Median filter denoising can easily destroy the continuity of events and lose detail (Chen et al, 2019). Diffusion filtering lacks the use of nonlocal information, which may damage some effective signals (Wang et al, 2021). Although frequency-domain denoising is one of the most commonly-used denoising methods, it cannot suppress noise in the frequency range that coincides with the effective signal (Sergio and Tad, 1988).…”