Method for picking seismic wave first break

Abstract

The embodiment of the invention provides a method for picking a seismic wave first break. The method comprises the following steps of: forming a new seismic attribute trace based on a complex seismic trace noise-adding method; and picking the seismic wave first break by using an energy ratio method. Compared with the conventional energy ratio method of the seismic attribute trace (an amplitude absolute value or an amplitude envelope), the method for picking the seismic wave first break has the characteristics of high anti-noise capacity and high picking accuracy.

Description

technical field [0001] The invention relates to a technology for automatically picking up the first arrival of seismic waves in petroleum geophysical exploration, in particular to a method for picking up first arrivals of seismic waves. Background technique [0002] In seismic exploration, the seismic wave excited from the shot point and first arriving at the receiver is called the first arrival wave, and this arrival time is called the first arrival time of the seismic wave, referred to as the first arrival of the seismic wave. Accurately picking up the first arrival of seismic waves is the basis for static correction and near-surface velocity tomography inversion. This work is more important for complex surface conditions such as deserts and mountains, and it is one of the keys to do a good job in subsequent seismic data processing. . [0003] Since picking up the first arrivals of seismic waves manually is time-consuming and labor-intensive, the automatic picking method ...

AI Technical Summary

Problems solved by technology

[0006] The fractal dimension method identifies the location of the first arrival of the seismic wave by counting the fractal dimension characteristics of the seismic trace curve. This method is very sensitive to the selection of the time window and step size, and a little carelessness will seriously affect the picking results.

[0007] The neural network method is an intelligent learning method that utilizes the attribute characteristics of multiple seismic trace first arrivals to identify the first arrival. Although this method has high precision, it is difficult to implement and the efficiency is low.

[0008] The correlation method uses the similarity characteristics of the first arrivals of adjacent seismic traces to pick up the first arrivals. This method has a good effect on suppressing noise, but for seismic records with complex waveform characteristics (such as the "continued" waveform of the first arrival ), the picking accuracy cannot be guaranteed;

[0009] The image method first grayscales the seismic data, and then uses image processing technology (such as edge detection technology) to identify the first arrival of the seismic wave. When there is strong interference on the seismic data, the accuracy of this method is difficult to guarantee

[0010] In summary, the existing methods for automatic first-arrival picking of seismic waves have the problems of poor anti-noise ability and low picking accuracy

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