Frequency division diffusion filter fault strengthening method based on optimized Morlet wavelet

Abstract

The invention provides a frequency division diffusion filter fault strengthening method based on optimized Morlet wavelet. The method comprises the following steps: converting post-stack seismic datainto an MATLAB identifiable data format; carrying out spectral analysis on seismic data and extracting the wavelet; improving a Morlet wavelet generating function and constructing a Morlet wavelet function composition; carrying out wavelet frequency division processing by utilizing the optimized Morlet wavelet; calculating a structural tensor of the post-stack seismic data and further constructingnew fault factors based on the structural tensor; under the protection of the structural tensor, carrying out anisotropic diffusion filter on frequency division seismic data; and carrying out waveletinverse transformation on a frequency division filter result and reconstructing to obtain improved signal-to-noise ratio and resolution ratio and protect the seismic data of fault information. The method has the advantages that the resolution ratio of the seismic data is improved while denoising and edge protecting are realized, so that the low-order fault recognition ability of the seismic datais improved.

Description

technical field [0001] The invention relates to the technical field of oil field development, in particular to a frequency division diffusion filter fault strengthening method based on optimized Morlet wavelet. Background technique [0002] In the process of seismic data acquisition, it is unavoidable to be affected by factors such as sensor sensitivity, field noise interference, and digital-to-analog conversion quantization problems, resulting in low signal-to-noise ratio of seismic data, which brings great difficulties to subsequent structural interpretation. Aiming at the problem of noise suppression in conventional post-stack seismic data, scholars at home and abroad have carried out a lot of research and proposed methods such as median filtering, Gaussian filtering, and time-frequency domain filtering. The above method is effective in noise suppression, but since the local direction information of seismic reflection cannot be recognized, the edge information will be des...

AI Technical Summary

Problems solved by technology

At present, Morlet wavelet has good local performance, and when the main frequency of Morlet wavelet is greater than 1Hz, it approximately satisfies the allowable condition. It is widely used in the wavelet frequency division of seismic data, but the traditional Morlet wavelet and the improved Morlet wavelet Both have the problem that their waveform and spectrum cannot match the waveform and spectrum of the seismic wavelet at the same time, resulting in low resolution of frequency division results

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