A feature preserving denoising approach for 3-D seismic data visualization
Zhumei Zhao, Gang Hua, Jinguang Sun, Gaohe Qiu · 2010
This paper focuses on suppressing noise while enhancing features for 3-D seismic visualization. To achieve this aim, we propose a 3D tensor based anisotropic diffusion. This algorithm uses a structure tensor to robustly estimate the local orientation of the geological structures. Then, the anisotropic diffusion is steered by these tensors, which smoothes noise away while enhance the features. In addition, the smoothing effect is adjustable by a contrast parameter. For the smoothed seismic data, we use high quality volume rendering algorithm to render it. In this paper, we discuss and implement our feature-enhance visualization algorithm and give the result.