Seismic Fault Preserving Diffusion

TL;DR

This paper introduces a novel seismic fault preserving diffusion method that enhances 3-D seismic data by denoising while maintaining fault structures, improving fault detection in geological imaging.

Contribution

It proposes a new non-linear diffusion filtering approach driven by structure tensor eigenvalues for better fault preservation in seismic data processing.

Findings

Effective on both synthetic and real data

Improves fault detection accuracy

Reduces noise without blurring faults

Abstract

This paper focuses on the denoising and enhancing of 3-D reflection seismic data. We propose a pre-processing step based on a non linear diffusion filtering leading to a better detection of seismic faults. The non linear diffusion approaches are based on the definition of a partial differential equation that allows us to simplify the images without blurring relevant details or discontinuities. Computing the structure tensor which provides information on the local orientation of the geological layers, we propose to drive the diffusion along these layers using a new approach called SFPD (Seismic Fault Preserving Diffusion). In SFPD, the eigenvalues of the tensor are fixed according to a confidence measure that takes into account the regularity of the local seismic structure. Results on both synthesized and real 3-D blocks show the efficiency of the proposed approach.