Linear noise removing method and device

Abstract

The invention discloses a linear noise removing method and device. The method comprises the steps that singular value decomposition is carried out on a common imaging point gather matrix to acquire a first singular value matrix and left and right feature matrixes; singular values of the first singular value matrix are sorted by descending order, and column vectors of left and right feature matrixes are correspondingly adjusted according to the order of the singular values; first m singular values of the sorted first singular value matrix are reserved; the singular values except m singular values are set to be zero to acquire a second singular value matrix, wherein m is the preset number of the singular values; according to the second singular value matrix and adjusted left and right feature matrixes, calculation is carried out to acquire a first common imaging point gather matrix after linear noise removing. According to the linear noise removing method and device, which are provided by the invention, a valid signal is reserved, and at the same time linear noise in a common imaging point gather is removed.

Description

technical field [0001] The invention relates to a seismic exploration data processing technology, in particular to a linear noise removal method and device. Background technique [0002] The main task of seismic exploration is to realize the imaging of underground geological structures and obtain corresponding seismic exploration data. Subsequent processing of the seismic exploration data is used to find out the underground geological structure. In the process of processing the seismic exploration data, it is usually necessary to use migration imaging technology to form a common imaging point gather; The quality of image shifting can be improved, and the underground geological structure can be restored to the greatest extent. [0003] However, in practical applications, it will be interfered by various noises when forming the common imaging point gather. The noise includes random noise and linear noise. Such as figure 1 As shown in , it shows a typical common image poin...

AI Technical Summary

Problems solved by technology

The disadvantage is that the effective signals in the cut-off area are also discarded, which affects the accuracy of residual picking

[0006] (2) Horizontal filtering method: Doing low-pass filtering in the horizontal direction can suppress oblique noise, but it also affects the residual signal and reduces the accuracy of residual picking

However, the differential algorithm will also cause the phase change of the effective signal, which will eventually cause the differential algorithm to seriously damage the effective signal.

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