Bilateral Filters with Elliptical Gaussian Kernels for Seismic Surveys Denoising

Abstract

Seismic surveys consist of large volumes of data acquired by a network of sensors. Such survey data is usually corrupted by different types of noise and other distortions. Under these conditions, the recovery of correct seismic amplitudes has become more challenging. The accuracy of such amplitudes is of primary importance in the pipeline of seismic interpretation. In this work, we introduce a seismic image denoising approach based on the Bilateral filter with Elliptic Gaussian kernel (BFEGK). The Bilateral filter is a non-linear filter that preserves edges and reduces noise. We enhance the filter for seismic image denoising by formulating an elliptic Gaussian kernel. We utilize the interquartile range to obtain a robust estimate of the noise standard deviation. Under Gaussian noise scenarios, the performance is compared to dictionary learning (DL) based denoising, standard Bilateral Filter with Noise Thresholding (BFMT), and Wavelet thresholding (WT) methods using real seismic images. Our proposed method exhibits the closest performance to the DL based denoising compared to GBF and WT methods with a significantly reduced computational load.