Seismic wave imaging method based on particle swarm optimization CRS (Common Reflection Surface) super gather

Abstract

The invention belongs to the field of seismic data processing for oil-gas exploration, and particularly relates to a seismic wave imaging method based on particle swarm optimization CRS (Common Reflection Surface) super gather. The method comprises the following steps of: step 1, optimizing distributed CRS superposition on pre-stack data, and updating three wave field parameters of a CRS superimposed surface of the superimposed final output by using a particle swarm optimization algorithm; step 2, performing curvature and inclination correction on the pre-stack data under the guidance of an optimal value of the CRS wave field parameter; step 3, inputting the optimal value of the CRS wave field parameters as a standard parameter, performing partial CRS superposition on the pre-stack data, and outputting a CRS optimized super-channel set; and step 4, performing CRS superposition and time offset on the output CRS optimized super gather to obtain an optimal CRS superposition profile and anoffset profile. The seismic wave imaging method based on the particle swarm optimization CRS super gather can effectively solve a problem that the inclination and curvature correction of the data arenot performed in the CRS superposition process in the prior art, so that the influence of the curvature and the inclination angle is amplified together with effective signals in a superposition process, thereby seriously affecting the imaging quality.

Description

technical field [0001] The invention belongs to the field of oil and gas exploration seismic data processing, and in particular relates to a CRS super-gather seismic wave imaging method based on particle swarm optimization. Background technique [0002] The common reflection surface element (CRS) superposition was first proposed by Professor Hurbal, based on the paraxial ray theory, through the CRS three parameters (α, R NIP , R N ) to superimpose the seismic data, taking all the reflections in the first Fresnel zone into consideration, improving the signal-to-noise ratio of the data and realizing more accurate imaging of complex subsurface media. Compared with the previous signal-based noise suppression algorithm, common reflection surface element (CRS) stacking has the advantage of effectively suppressing noise and improving the signal-to-noise ratio in the imaging process. Or it has broad application prospects in the exploration of unconventional oil and gas reservoirs....

AI Technical Summary

Problems solved by technology

However, because the traditional CRS superposition process is relatively simple, and the data is not corrected for inclination and curvature, the influence of curvature and inclination in the processing results may be amplified together with the effective signal during the superposition process, that is, the data error is superimposed and amplified. The impact of imaging accuracy and effect cannot be ignored

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