Method for improving velocity spectrum resolution by using phase information

Abstract

The invention relates to a technique for processing petroleum geophysical prospecting seismic data, which is a method for improving the velocity spectrum resolution by using phase information. The method comprises the following steps of: stimulating and recording seismic waves to obtain a common midpoint gather; performing a first-order derivative operation along a time direction, and performing Hilbert transform to obtain a complex seismic trace; superimposing P(t) traces to obtain the optimal velocity of normal moveout correction (NMO); calculating a velocity spectrum by using a conventional velocity analysis method; and performing side lobe suppression on the selected common midpoint (CMP) for velocity analysis and each time sample vector in SPS to finally obtain the velocity spectrum. By the method, influences on the velocity analysis due to amplitude changes can be avoided, the distribution of noises is converted into the distribution with gauss distribution characteristics, so that influences on the velocity analysis due to abnormal noises can be effectively inhibited, and the resolution of the velocity spectrum and the accuracy of the velocity analysis are improved.

Description

technical field [0001] The invention relates to a processing technology of petroleum geophysical exploration seismic data, which is a method for improving velocity spectrum resolution by using phase information, and further improving seismic parameter estimation reliability and imaging precision. Background technique [0002] Seismic exploration is to artificially excite and generate seismic waves on the surface, use single-component or multi-component sensors to record the formation response of seismic waves on the surface or underground, and study their propagation laws in the formation to find out the underground geological structure and lithology changes. Geophysical prospecting methods for oil and gas fields. [0003] Seismic exploration is mainly divided into three links: acquisition, processing and interpretation. Among them, seismic acquisition is the process of setting up and deploying observation systems for exploration targets in the field, artificially exciting ...

AI Technical Summary

Problems solved by technology

Among them, the superposition method uses the dynamically corrected gather superposition energy or similarity coefficient as the judgment criterion to calculate the velocity spectrum. This type of algorithm is simple, easy to implement, and relatively high in efficiency. It is currently the most widely used method. The cross-correlation operation of different channel signals is used to determine and calculate the velocity spectrum, but the anti-interference ability of this method is poor, and the calculation amount is relatively large

In addition, whether it is the stacking method or the correlation method, they ignore the variation characteristics of the amplitude with the offset, and the resolution of the velocity spectrum is relatively low. Especially when the amplitude variation characteristics of the seismic data are relatively severe, these methods will Introduces a large bias to velocity analysis

[0007] The optimization method usually uses the eigenvalues ​​of the covariance matrix composed of seismic data to estimate the signal-to-noise ratio and construct the energy function, and obtain the velocity spectrum information through the optimal estimation. This method avoids some defects of the conventional stacking method (considering The effect of noise on amplitude change), is sensitive to the correlation of signals between channels in the window, and can obtain high velocity resolution for events that can be separated easily, but this type of method is vulnerable to strong noise, interference in phase Due to the influence of the singularity of the axis and window boundary, some false secondary energy clusters are likely to be generated in the velocity spectrum, which affects the reliability of velocity analysis. At the same time, matrix decomposition and optimization estimation are required when calculating eigenvalues. The amount of calculation is relatively large

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