Primary and Multiple Separation Method Based on Alternate Splitting Bregman Iterative Algorithm

Abstract

The invention belongs to the field of seismic signal processing in seismic exploration technologies, and specifically discloses a primary wave and multiple wave separation method based on an alternative splitting Bregman iterative algorithm. With regard to a multiple wave self-adaptive subtraction method based on a 3D matched filter, the primary wave and multiple wave separation method utilizes the alternative splitting Bregman iterative algorithm to solve an optimization problem of applying sparsity constraint on primary waves, achieves the estimation of the 3D matched filter, and utilizes the estimated 3D matched filter to separate the primary waves and the multiple waves in a 3D data window in a self-adaptive manner. Compared with the traditional iterative reweighted least squares algorithm, the alternative splitting Bregman iterative algorithm adopted by the primary wave and multiple wave separation method only needs to calculate matrix-matrix multiplication and matrix inversion once when estimating the 3D matched filter at each 3D data window, can effectively reduce the calculation complexity of solving the optimization problem, and improves the calculating efficiency of primary wave and multiple wave self-adaptive separation.

Description

technical field [0001] The invention belongs to the field of seismic signal processing in seismic exploration technology, and in particular relates to a primary wave and multiple wave separation method based on an alternately split Bregman iterative algorithm. Background technique [0002] SRME (Surface Related Multiple Elimination) is a multiple suppression method widely used in marine seismic exploration. There are time and space differences between the multiples predicted by SRME and the real multiples, and the matched filter can be used to adaptively subtract the predicted multiples from the original data. In general, as the dimensionality of the matched filter increases (from 1D, 2D to 3D matched filters), multiples are better adaptively subtracted from the raw data, but require more computation time. The mathematical model of multiple wave adaptive subtraction method based on 3D matched filter is (Li, Z., and W.Lu, 2013, Adaptive multiple subtraction based on 3D blind...

AI Technical Summary

Problems solved by technology

However, the iterative reweighted least squares algorithm needs to calculate a matrix-matrix multiplication and matrix inversion at each iteration step, and the computational complexity is relatively high

Images