Linearity and nonlinearity integrated seismic wavelet extracting method based on high-order statistics

Abstract

The invention discloses a linearity and nonlinearity integrated seismic wavelet extracting method based on high-order statistics, which is characterized in that a linearity and nonlinearity combined method is utilized to solve seismic wavelet parameters after fitting objective function is established on an ARMA (auto regressive moving average) model. The method provided by the invention comprises the following steps of: firstly adopting a cumulant matrix equation method to initially estimate a wavelet model; then applying the obtained wavelet pre-estimated value in determination of an initial parameter searching space by a fitting optimization algorithm; on the basis, utilizing a cumulant fitting error to adjust a threshold value in the matrix equation method and search an accurate optimizing interval and further obtain the accurate order and parameter value of the model through the cumulant fitting method. The wavelet extracting method provided by the invention has high noise immunity, can extract the seismic wavelets with high accuracy in shorter time and has good application value in practical seismic data processing.

Description

Technical field: [0001] The invention belongs to the field of seismic signal processing. Background technique: [0002] Seismic wavelet estimation is one of the key issues in seismic signal processing, and it is the basis of seismic processing techniques such as seismic forward modeling, seismic inversion and deconvolution. High-precision seismic wavelet estimation has important practical value, and is an important research topic to be solved urgently in high-resolution processing technology in the field of oil and gas seismic exploration. [0003] The main motivations and starting points for using higher-order statistics in the fields of signal processing and system theory can be summarized as: (1) to suppress the influence of additive colored noise (whose power spectrum is unknown); (2) to identify non-causal, non-minimum phase systems Or reconstruct the non-minimum phase signal; (3) Extract various information caused by Gaussian deviation; (4) Detect and characterize the...

AI Technical Summary

Problems solved by technology

However, these two methods have their own defects: Compared with the nonlinear parameter estimation method (cumulant fitting method), the linearized parameter estimation method (cumulant matrix equation method) has a faster operation speed, but because only seismic Record the special slice information of cumulants. When wavelet extraction is performed on high-noise-signal-ratio and short-data seismic records, this method may have large estimation errors; the cumulant fitting optimization method makes full use of the cumulant information of seismic records , the wavelet extraction has higher accuracy and numerical stability, but it is difficult to determine the initial parameter range of the model with the cumulant fitting method, which seriously affects the optimization efficiency of the algorithm

Although relatively stable, this method is extremely difficult to obtain model parameters

This is because the objective function of cumulant fitting is mostly a nonlinear function with multiple dimensions and peaks, and the usual optimization algorithm is powerless to solve the objective function

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