Channel Segmentation using Confidence and Curvature-Guided Level Sets on Noisy Seismic Images

This paper presents a new method for segmenting channel features from commonly noisy 3D seismic images. Anisotropic diffusion using Gaussian-smoothed first order structure tensors is conducted along the strata of seismic images in a way that filters across discontinuous regions from noise or faulting, while preserving channel edges. The eigenstructure of the second order structure tensor is used to generate an estimation of orientation and channel curvature. Gaussian smoothing of second order tensor orientations accounts for noisy vectors from imprecise finite difference calculations and generates a stable tensor across the image. Analysis of the confidence and direction of second order eigenvectors is used to enhance depositional curvature in channel features by generating a confidence and curvature attribute. The tensor-derived attribute forms the terms of a PDE, which is iteratively updated as an implicit surface using the level set process. This technique is tested on two 3D seismic images with results that demonstrate the effectiveness of the approach.