Earthquake focus output signal phase distortion detecting method based on wavelet transformation

Abstract

An earthquake focus output signal phase distortion detecting method based on wavelet transformation comprises the following steps that (a) an earthquake focus output signal with random noise is obtained; (b) a wavelet basis function and decomposition level are selected for the earthquake focus output signal to obtain a wavelet coefficient after output signal decomposition; (c) an absolute value ofa wavelet coefficient value (shown in the description) is compared with a threshold value (shown in the description); (d) inverse wavelet transformation processing is conducted on the wavelet coefficient value (shown in the description) obtained after the threshold value processing to obtain an output signal after noise suppression; (e) zero-crossing detection is conducted on the output signal after noise suppression, and an output value is assigned, namely phase detection is completed. By conducting wavelet filtering suppression on the output signal with random noise and then detecting a phase through zero-point phase detection, the suppression of random noise can be realized. The algorithm can process data quickly and protect the signal details well, the problems of zero drift and jumping occurring during the zero-crossing detection can be solved, and thus accurate phase detection is achieved.

Description

technical field [0001] The invention relates to the technical field of detection of seismic source output signals, in particular to a method for detecting phase distortion of seismic source output signals based on wavelet transform. Background technique [0002] When the exploration source is working, the coupling between the excitation end and the ground is often poor, or the excitation end is disturbed by human movement, etc., resulting in a large phase distortion between the actual control signal and the output signal of the source, which affects the signal-to-noise ratio of the signal and reduces the signal. Accuracy of test results. In view of this situation, most of the current phase detection adopts the zero-crossing method, but in the case of random noise, the zero point detection results have offset and jumping phenomenon, which seriously affects the accurate detection of the phase. Noise suppression methods mainly include methods based on mathematical statistical ...

AI Technical Summary

Problems solved by technology

[0002] When the exploration source is working, the coupling between the excitation end and the ground is often poor, or the excitation end is disturbed by human movement, etc., resulting in a large phase distortion between the actual control signal and the output signal of the source, which affects the signal-to-noise ratio of the signal and reduces the signal. Accuracy of test results

In view of this situation, most of the current phase detection adopts the zero-crossing method, but in the case of random noise, the zero point detection results have offset and jumping phenomenon, which seriously affects the accurate detection of the phase. Noise suppression methods mainly include methods based on mathematical statistical significance and adaptive filtering methods

The adaptive filtering method can adapt well to changes in the environment, and adaptively change the filtering parameters to suppress random noise, but this algorithm is complex and affects the exploration efficiency

The algorithm of the low-pass filtering method is simple, but useful signals will be filtered out when there is frequency overlap between the signal and the noise. CN103344988A discloses a "phase detection method for vibroseis signals based on K-L decomposition" which uses a method in the sense of mathematical statistics to analyze the noise. suppression, but this method is not suitable for signals with poor stationarity

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