Rapid quasi-newton method-based full-waveform inversion method

Abstract

The invention discloses a rapid quasi-newton method-based full-waveform inversion method, belongs to the technical field of seismic exploration speed modeling, and aims at improving the convergence rate of a quasi-newton method so as to improve the calculation efficiency of seismic full-wave inversion and reduce the calculated amount. The method comprises the steps of defining a group of weightingcoefficient sequences to construct a parameter model iterative formula; importing a middle iterative parameter model and calculating a quasi-newton iterative direction and an optimum step length on the basis of the parameter model; and updating an original parameter model and the middle iterative parameter model by utilizing weighting coefficients, the iterative direction and the optimum step length. According to the method, calculation is carried out to obtain parameter models closest to optimum solutions, and the parameter models are used for the calculation of the next iteration directionand optimum step length, so that the convergence rates of target functions are improved. In the iterative calculation of each time, more calculated amount is not added in the method, and only a smallamount of multiplication and addition with relatively low complexity are carried out, so that the calculated amount of the full-waveform inversion is effectively reduced.

Description

technical field [0001] The invention belongs to the technical field of seismic exploration velocity modeling, and relates to a full waveform inversion method based on a fast quasi-Newton method. Background technique [0002] Theoretically, the full waveform inversion technology has the ability to reveal the geological structure and reservoir parameters under complex geological conditions, and the full waveform inversion in the frequency domain can realize high-precision parameter modeling by using only a few discrete frequency data. For the solution of the full waveform inversion problem, the nonlinear local optimization algorithm is mainly used, namely the gradient method and the Newton method. Even though the gradient method is simple in structure and easy to implement, it only uses gradient information in the calculation process and has first-order convergence. The Newton-like method considers the gradient information of the objective function and the Hessian matrix at t...

AI Technical Summary

Problems solved by technology

Although the quasi-Newton method avoids the direct calculation of the Hessian matrix and effectively reduces the time and memory consumption, due to the limitation of computing resources, the full waveform inversion technology is always limited to the application in the two-dimensional case

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