Stratum structure self-adaption median filtering method

Abstract

The invention discloses a stratum structure self-adaption median filtering method. The method comprises the steps of: 1, calculating a gradient structure tensor; 2, calculating a stratum lateral incontinuity measurement; 3, structuring a structure self-adaption median filtering device; and 4, performing structure self-adaption median filtering and processing. By utilizing the method, earthquake random noise and part coherent noise can be effectively attenuated, and stratum edge and detailed structure characteristics such as effective signals, geologic faults and amplitude abnormalities can be maintained in a filtering process; the algorithm of the technical scheme is easy to realize and has good operability; and moreover, a selection problem of a median filtering processing window size is avoided, the method meets the requirements of the median filtering processing of complicated signals, for data areas with good event extension, the filtering performance of the median filtering device is improved through adaptively controlling the direction characteristics of a filtering window, and for areas with geologic faults and angular unconformity, the maintaining performance of the median filtering device to stratum edge structures such as the geologic faults is improved through adaptively adjusting the size of the filtering window.

Description

technical field [0001] The invention belongs to the technical field of seismic exploration, and relates to a seismic random noise attenuation method, in particular to a seismic random noise attenuation method using a stratum structure self-adaptive median filter. Background technique [0002] In oil and gas exploration and development, structural features such as faults, channels and fractures, and sedimentary features such as channel sand bodies are the basis for discovering and describing reservoirs. It is of great significance to distinguish and analyze these structural features in seismic data. However, due to the complexity of geological forms in these areas, these areas in seismic data are susceptible to noise interference, and it becomes difficult to describe and characterize these areas no matter whether manual interpretation or automatic detection algorithms such as coherence and edge detection are used. Traditional seismic data denoising methods, such as dip filter...

AI Technical Summary

Problems solved by technology

[0016] (1) When the sub-window size of the two-dimensional multi-stage median filter is too large, it cannot match the signal structure characteristics in the neighborhood of the data to be processed, which will obviously suppress the effective signal;

[0017] (2) When the sub-window size of the two-dimensional multi-stage median filter is too small to match the signal structure characteristics in the neighborhood of the data to be processed, the noise attenuation ability will be significantly reduced

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