Wavelet iterative estimation based finite difference contrast source full waveform inversion method

Abstract

The invention discloses a wavelet iterative estimation based finite difference contrast source full waveform inversion method. The method comprises the steps of S1, inputting an initial velocity model, constructing a finite difference operator and calculating a background wave field; S2, calculating an approximate seismic focus ratio, calculating a scattering field thereby, and acquiring a contrast source initial value, a contrast initial value and a total wave field initial value; S3, calculating a gradient value corresponding to the initial contrast source at each shot, and constructing a conjugate gradient direction; S4, solving the update step length of the contrast source at each shot, and performing iterative update on the contrast source; S5, judging the size of an objective function value and an allowable error at the moment so as to decide whether iteration stops or not; and S6, calculating the propagation velocity of waves in a medium according to the current contrast value. The method disclosed by the invention effectively overcomes a problem that a result acquired by inversion seriously deviates from a real model when the difference between applied wavelets and actual wavelets is great, and the calculation amount is significantly reduced.

Description

technical field [0001] The invention relates to a finite difference contrast source full waveform inversion method based on wavelet iterative estimation, and belongs to the technical field of frequency domain acoustic wave full waveform inversion. Background technique [0002] Full waveform inversion (FWI) extracts velocity parameters based on the dynamic characteristics of seismic data, which is expected to obtain more accurate subsurface velocity structures compared with methods based on data kinematic characteristics such as travel time tomography. At present, from the perspective of implementation methods, it is mainly divided into two categories, time domain and frequency domain. Among them, in the frequency domain, only a few frequencies are needed to obtain high-precision inversion results, and it is easy to realize multi-scale calculations and has been rapidly popularized. The main calculation amount of full waveform inversion in the frequency domain lies in a large...

AI Technical Summary

Problems solved by technology

The main calculation amount of full waveform inversion in the frequency domain lies in a large number of forward modeling simulations, which need to solve a huge linear equation system. Because the direct method (such as LU decomposition) has the advantage of calculating multiple shots, it has been widely used. Its main The amount of calculation is the decomposition operation of the matrix, and the amount of calculation is relatively large

At the same time, due to the lack of accurate source wavelet information, when the difference between the simulated wavelet and the real wavelet is too large, the inversion results will completely deviate from the real model

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