Desert area seismic exploration denoising method based on self-adaptive resharpening diffusion

Abstract

The invention relates to a desert area seismic exploration denoising method based on self-adaptive resharpening diffusion, which belongs to a noise suppression method. By means of structure tensor, adiffusion coefficient of resharpening diffusion filtering in the gradient direction is constructed so as to non-linearly vary with the degree of consistency of seismic exploration data, and thereby the diffusion degrees of the signal region and noise region of the seismic exploration data are controlled; on the basis, the feature vector of the structure tensor is further utilized to regulate the diffusion coefficient in the consistent direction, so that the intensity of diffusion can vary with the change of different seismic areas, and thereby the retention of resharpening diffusion on amplitudes and structures of events is enhanced. A synthetic data and actual desert area record processing result indicates that the method can effectively suppress random desert noise with similar waveformsand bands as seismic exploration signals, moreover, the retention effect on seismic exploration signals is improved, the recovery of steep events is clearer and more accurate in particular, and the method has practicability and effectiveness.

Description

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AI Technical Summary

Benefits of technology

This technology helps reduce noisy signals from traditional surveying techniques for studying geology or mineral resources more accurately than previously possible due to its ability to effectively filter out unwanted sounds without affecting useful details about them.

Problems solved by technology

The technical problem addressed in this patents relating to current techniques for reducing noise in drilling operations involves identifying important ground properties (such as roughness) within an image region during sedimentation processes. These conventional approaches involve analyzing randomly distributed noise patterns in the image regions themselves without taking advantage of specific spectral relationships between them. Additionally, they often require complicated calculations involving multiple steps before achieving desired levels of noise removal.

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