Frequency Diffusion Diffusion Filtering Fault Enhancement Method Based on Optimized Morlet Wavelet

Abstract

The invention provides a frequency division diffusion filter fault strengthening method based on optimized Morlet wavelet, including: converting post-stack seismic data into a data format recognizable by MATLAB; performing frequency spectrum analysis on seismic data and extracting wavelet; improving Morlet wavelet mother function to construct a combination of Morlet wavelet functions; use optimized Morlet wavelets for wavelet frequency division processing; calculate the structural tensor of post-stack seismic data, and then construct a new fault factor based on the structural tensor trace; under the protection of the structural tensor trace , anisotropic diffusion filtering is performed on the frequency-division seismic data; wavelet inverse transform is performed on the frequency-division diffusion filtering results, and reconstruction is obtained to improve the signal-to-noise ratio and resolution, and to protect the fault information seismic data. This method can improve the resolution of seismic data while denoising and preserving edges, thereby improving the ability to identify low-order faults in seismic data.

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

Images