High resolution seismic wavelet extracting method based on high-order statistics and ARMA (autoregressive moving average) model

Abstract

The invention relates to a high resolution seismic wavelet extracting method based on high-order statistics and an ARMA (autoregressive moving average) model, which belongs to the field of seismic signal processing. The high resolution seismic wavelet extracting method provided by the invention is characterized in that: under the precondition of performing ARMA parameter simplified modeling on seismic wavelets, SVD (singular value decomposition) based on autocorrelation function is adopted to determine an order of AR part, an MA order determining method is provided for integrating information content criterion function in a high-order cumulant MA order determining method, and the MA order determining accuracy rate in a seismic wavelet ARMA model is improved; an SV-TLS (singular value decomposition - total least squares estimation) and a cumulant method are respectively adopted to estimate wavelet parameters; and under the precondition of ensuring wavelet precision, the model order is decreased as far as possible to improve the operation efficiency and to finally realize seismic wavelet extraction in high efficiency and high precision. Through the data simulation verification and the practical seismic data processing demonstration, the method provided by the invention is proved to effectively improve the estimated precision and extracting efficiency for the seismic wavelets and to have obvious effect even under short-time seismic data and strong noise pollution.

Description

technical field [0001] The invention belongs to the field of seismic signal processing. Background technique [0002] Nowadays, the exploration and development of oil and gas fields are developing in the direction of small-scale and thin reservoirs, and the precision requirements for seismic exploration are getting higher and higher. In order to adapt to the dynamic prediction of oil and gas reservoirs and search for complex structural and lithological oil and gas reservoirs, the sections after seismic processing are required to have the "three highs" characteristics of high signal-to-noise ratio, high resolution and high fidelity. [0003] Seismic exploration includes three links: field acquisition, indoor processing and seismic data interpretation. In order to improve the resolution of seismic records, it is necessary to work hard on each link of acquisition, processing and interpretation, and tap the capabilities of each link to achieve high quality in each link, and fin...

AI Technical Summary

Problems solved by technology

[0005] The statistical wavelet extraction method uses signal processing technology to perform statistical processing on limited seismic data records, and makes full use of the amplitude, phase, frequency and other information of seismic data records to extract more applicable seismic wavelet estimates. Some assumptions are made about the distribution of the seismic data used and the subsurface reflection coefficient series (minimum phase, zero phase, maximum phase)

In fact, the seismic wavelet is often a mixed phase wavelet, and this simple statistical wavelet extraction method based on autocorrelation cannot theoretically obtain accurate results.

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