Weighted Local Cross-Correlation Time-Frequency Domain Phase Inversion Method

Abstract

The invention provides a weight-based local cross-correlation time frequency domain phase inversion method via which time frequency domain phase information of seismic data is introduced into a cross-correlation objective function. The method is called the weight-based local cross-correlation time frequency domain phase inversion method. The introduction of the phase information alleviates the dependence of full waveform inversion on an initial velocity model. At the same time, weighting factors are added in the time frequency domain objective function, and anti-noise ability and stability ofthe inversion can be greatly increased. In a low frequency band weight-based local cross-correlation time frequency domain phase inversion method, a good initial velocity model can be obtained and canbe then used in a high frequency band weight-based local cross-correlation time frequency domain phase inversion method, and high resolution inversion results can be obtained. Tests in which low frequency components and strong Gaussian background noise are missing show that the weight-based local cross-correlation time frequency domain phase inversion method is advantaged by strong anti-noise ability, capability of being independent of the initial velocity model and the like.

Description

technical field [0001] The invention relates to a geophysical prospecting method, in particular, using the time-frequency domain phase information of seismic data to construct the target function of the velocity model inversion, and using the amplitude information of the time-frequency domain analog data to perform energy correction on the phase target function, eliminating Local cross-correlation time-frequency domain phase inversion method for problems such as unstable factors and waveform matching dislocation in the inversion process. [0002] Background technique: [0003] Full waveform inversion is a least-squares optimization problem. By continuously reducing the residual error between observed data and simulated data, the pre-set initial velocity model is updated, and the goal of high-precision inversion is finally achieved. In theory, the full waveform inversion method is one of the most accurate methods for obtaining subsurface velocity models compared to many existi...

AI Technical Summary

Problems solved by technology

However, when this method is applied to a complex velocity model, such as the Marmousi velocity model, the velocity model obtained by traveltime inversion of the wave equation still cannot meet the initial model requirements of conventional full waveform inversion.

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