Orbit period identification method based on wavelet transform modulus maxima algorithm

Abstract

The invention relates to an orbit period identification method based on a wavelet transform modulus maxima algorithm, and belongs to the technical field of geophysics, and aims at solving the problems that in the prior art, intuitive qualification and intuitive quantification are different, and implementation is difficult. The method comprises the following steps that 1, a continuous wavelet transform coefficient matrix of a one-dimensional well logging curve model is solved; 2, an absolute value of the wavelet transform coefficient matrix obtained in the step 1 is solved; 3, an average value of each line of the absolute value matrix obtained in the step 2 is solved, and then the average values form a column vector; 4, a sequence extreme point of the column vector obtained in the step 3 is solved; 5, the scale corresponding to the extreme point is a response of a preferred period, and the size of the preferred period is converted through a corresponding relation expression of the frequency and the scale in wavelet analysis; 6, an energy spectrum diagram is obtained through wavelet analysis, and then the ratio of the energy ring number is obtained. According to the method, modulus maxima operation is conducted with the help of the continuous wavelet transform energy spectrum diagram, and therefore not only are intuitive qualification and intuitive quantification combined, but also implementation is convenient and rapid.

Description

technical field [0001] The invention relates to an orbit period identification method based on wavelet transform modulus extremum algorithm, and belongs to the technical field of geophysics. Background technique [0002] Wavelet transform theory is a new signal processing technology developed in the past ten years. Because it can achieve high resolution in time domain and frequency domain, it is called "mathematical microscope". The one-dimensional continuous wavelet transform involved in the present invention can transform the one-dimensional well logging signal into two-dimensional information energy spectrum in the time domain and scale domain. Although the spectrum structure of the signal can be expressed intuitively, due to the data redundancy in the energy spectrum Phenomena, it is difficult to accurately point out its frequency (period) quantitative value. In the prior art, scholars at home and abroad have done a lot of research on extracting orbital period informati...

AI Technical Summary

Problems solved by technology

However, in the past, either there was no detailed discussion on how to quantitatively obtain periodic information from the one-dimensional continuous wavelet transform spectrum, or it was necessary to combine fast Fourier transform (FFT) for spectrum analysis, or to apply discrete wavelet transform, but it was necessary to study how to determine the number of decomposition layers question

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