Random noise attenuation of 3D multi-component seismic data using fast adaptive prediction filter

Abstract

Random noise in seismic records affects the accuracy of effective signal identification, making it difficult for subsequent seismic data processing, imaging, and interpretation. Therefore, random noise attenuation has always been an important step in seismic data processing, especially for 3D data. In recent years, multi-component exploration has been developed rapidly. However, the common method for processing multi-component data is to process each component separately resulting in the correlation between multi-component data being neglected. For 3D multi-component data, we propose a multi-component adaptive prediction filter (MAPF) based on noncausal regularized nonstationary autoregressive models to implement random noise attenuation in the t- x- y domain. The MAPF for multi-component signals can be used to identify the potential correlations and differences between each pair of components, providing not only a robust analysis of individual components but also effective information about the consistency and differences between each component with more information and constraints compared to traditional single-component prediction. Moreover, it can obtain smooth non-stationary prediction coefficients by solving the least squares problem with shaping regularization. The example results demonstrate that the MAPF method is superior to the traditional adaptive prediction filtering (APF) method. Furthermore, since the multi-component method requires more coefficients and takes longer time to predict than the single-component method, we further propose a fast multi-component adaptive prediction filter (FMAPF) combining the data pooling and coefficient reconstruction strategies. The example results demonstrate that the FMAPF method is effective at denoising and greatly improves computational efficiency. The method comes with a slight decrease in computational accuracy.