Random noise attenuation of 3D multicomponent seismic data using a 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, multicomponent exploration has been developed rapidly. However, the common method for processing multicomponent data is to process each component separately resulting in the correlation between multicomponent data being neglected. For 3D multicomponent data, we develop a multicomponent 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 multicomponent signals can be used to identify the potential correlations and differences between each pair of components, providing not only a robust analysis of the individual components but also effective information about the consistency and differences between each component with more information and constraints compared with traditional 1C prediction. Moreover, it can obtain smooth nonstationary 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 method. Furthermore, because the multicomponent method requires more coefficients and takes a longer time to predict than the 1C method, we further develop a fast MAPF (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.