High-precision dip estimation method

Abstract

The invention discloses a high-precision dip estimation method, which comprises the following steps of: firstly, calculating a gradient vector; then, turning over a vector field; calculating the vector set distance; calculating vector filtering weighting; and finally, carrying out vector weighting filtering processing. According to the method, the space consistency of dip estimation in a lineup single-direction extending area can be kept, and the dip estimation precision of the dip estimation method on a fault and the stratum structure with angular unconformity and fold morphology is improved. The algorithm content of the technical scheme is easy to realize and has high calculation efficiency. Meanwhile, the size of an analysis window can be flexibly regulated to satisfy the requirement of different dip estimation effects; for seismic data with severe noise interference, the space consistency of the dip estimation result can be improved by adopting a big analysis window; and for seismic data with a high signal to noise ratio, the dip estimation result precision of the fault and the angular unconformity area can be improved by adopting a small analysis window.

Description

technical field [0001] The invention belongs to the technical field of seismic exploration, and relates to a method for estimating stratum dip, in particular to a method for estimating stratum dip and azimuth with high precision using a weighted vector direction filter. Background technique [0002] The dip and azimuth of the seismic event are important products of 3D seismic data interpretation following the time structure and horizon picking and mapping. It is used for the interpretation of the exploration work area, and as an intermediate processing result, it can be used for subsequent seismic data processing and interpretation. The dip and azimuth pairs at each sampling location in a 3D seismic data volume define a local stratigraphic plane, allowing the analysis of stratigraphic discontinuities along this local plane, and the use of local stratigraphic continuity metrics to guide data filtering. Using the estimated dip and azimuth attributes, Barnes developed a series...

AI Technical Summary

Problems solved by technology

[0023] (1) When encountering seismic data with complex stratigraphic structures or low signal-to-noise ratios, the three eigenvalues ​​of the gradient structure tensor are often relatively close in amplitude, and there is no obviously larger eigenvalue, which makes it difficult to determine the local stratum direction estimation;

[0024] (2) When encountering faults, angle unconformities, and fold-shaped formation structures, the gradient structure tensor method cannot accurately estimate the direction of the formation structure where the analysis point is located because there is no clear extension direction of the formation in the local analysis neighborhood

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