Multi-channel matching tracking method based on seismic data

Abstract

The invention discloses a multi-channel matching tracking method based on seismic data. The method includes the steps that based on other seismic channels adjacent to one single seismic channel fL(t) in one group of seismic channels {f1(t), f2(t), ... , fL(t)}, the single seismic channel fL(t) is decomposed many times according to the equation (2) so as to obtain a plurality of sub waves with the formula shown in the specifications, the sub wave with the formula shown in the specifications is obtained in the nth decomposing, is the basic sub wave shared by all the seismic channels in the group of seismic channels {f1(t), f2(t), ... , fL(t)}, and is in the mode of the Morlet sub wave, L is the number of the seismic channels in the group of seismic channels, N is the decomposing number, n is an integer from 1 to N, a{l, n} is the amplitude of the wavelet obtained in the nth decomposing, and R<N> fL(t) is the final remaining seismic channel. The nth decomposing includes the steps that an initial basic sub wave is calculated with a single channel matching method; second, fine calculating is performed on the initial basic sub wave with the multi-channel matching tracking method; third, the amplitude of each sub wave is calculated.

Description

technical field [0001] The invention relates to a method for processing seismic signals in the field of petroleum geophysical exploration. Background technique [0002] The tracking matching technique selects the wavelets through an iterative method (tracking), and decomposes the seismic trace into a series of wavelets matching its time-frequency characteristics. For seismic reflection signals, Morlet wavelets are usually selected, which can better characterize the energy attenuation and velocity dispersion caused by elastic waves propagating in porous media. Firstly, preliminary wavelet parameter estimation is carried out, and then local optimization is carried out to select parameters to improve the efficiency of wavelet adaptive selection; the calculation of this method also needs to apply the corresponding analytical expressions of complex seismic traces and their attributes. For continuous wavelets, one of the important parameters is the wavelet decomposition scale, wh...

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Problems solved by technology

However, when the matching pursuit method is applied to actual seismic data, there is still the problem of non-uniqueness of the decomposition. Due to the non-uniqueness of the solution, the noise seriously affects the decomposition results.

Figure 2a Shown is a seismic section, each trace resolved individually using a matched trace, Figure 2b is the profile of wavelet summation, which shows that the wavelets of adjacent seismic traces extracted by the single-trace matching pursuit method lack spatial continuity, which makes the spectrum lack spatial continuity along the seismic section

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