Noise Attenuation of Seismic Data Using Anisotropic Diffusion Method

Having high quality images of seismic sections is one of the most important issues for interpreters. In order to delineate geological features and sedimentary packages such as faults, channels and etc., many attempts carried out to attenuate unwanted noises which caused by improper environmental and acquisition parameters and processing artifacts.

In this paper, we propose a method based on Anisotropic Diffusion which is aiming at reducing noise without destroying significant parts of the image content, typically edges or other details that are important for the interpretation. Anisotropic diffusion is a process that produces a family of parameterized images. Each resulting image is a convolution between the original image and a 2D isotropic Gaussian filter that depends on the local content of the original image. Accordingly, anisotropic diffusion is a non-linear and space-variant transformation of the basic image. As this technique has special abilities in noise attenuation and preserve structures in the filtered image as well, it can be utilized for seismic data enhancement. Efficacy of this method in enhancing data quality and in highlighting small features is presented using synthetic and real data examples.