Adaptive dual-domain filtering for random seismic noise removal

Abstract

Random noise significantly reduces the signal-to-noise ratio of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we use the variance of random noise as the level of random noise and develop an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we use a difference operator in two directions to remove the useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, the seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in synthetic examples and field data when compared with two typical denoising methods: f- x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.