Decomposition method of attenuation signal based on q-value difference reflection

Abstract

The invention provides a decay signal decomposition method based on Q value difference reflection, comprising the following steps: selecting a first matching atom of a to-be-decomposed signal from an over-complete dictionary; decomposing the signal into a component on the first matching atom and a residual; iteratively decomposing the residual obtained through decomposition and judging the residual energy; and when the residual energy is less than a threshold, determining that decomposition is completed. Through the technical scheme, basis functions of a matching pursuit algorithm are constructed based on Q value difference reflection and a frequency domain seismic convolution model, and a decay seismic signal is decomposed using the set of basis functions, so that decay sub-signals of the seismic signal can be reconstructed to the maximum degree, a result of higher precision can be provided for subsequent rock physics analysis, decay parameter estimation or spectrum analysis, and the reliability and accuracy of reservoir oil-gas possibility prediction are improved.

Description

technical field [0001] The invention relates to the field of petroleum geophysical exploration, in particular to an attenuation signal decomposition method based on Q value difference reflection. Background technique [0002] In the traditional seismic exploration process, the attenuation signal contains a wealth of information, including thin layer thickness information, formation attribute information and gas-bearing information. Various time-frequency transformation methods are usually used to describe the non-stationary time signal of the decay signal, including short-time Fourier transform (DFT), maximum entropy method (MEM), continuous wavelet transform (CWT) and matching pursuit decomposition ( MPD) and other methods. The above methods have their own advantages and disadvantages, but none of them can fully utilize the characteristics of seismic convolution forward modeling to obtain more accurate time-frequency spectrum. [0003] Both the short-time Fourier transfor...

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