Inversion model updating method based on M-H sampling of Gaussian distribution

Abstract

The invention discloses an inversion model updating method based on M-H sampling of Gaussian distribution and relates to the technical field of geophysical inversion. The method comprises the following steps of: step 1, pre-processing earthquake information to obtain an initial model of the parameters to be inverted; step 2, selecting the initial model of the t-th parameter to be inverted to calculate an initial wave impedance logarithm L1; step 3, calculating the variance of the initial wave impedance logarithm L1, establishing a state transition function obeying a Gaussian distribution, andrepeatedly updating the initial wave impedance logarithm L1 according to the state transition function; and step 4, determining whether t is greater than the seismic trace in the earthquake information; if so, ending the update; and if not, causing t=t+1 and jumping to step 2 to continue the inversion. The inversion model updating method based on M-H sampling of Gaussian distribution solves the problems of a too large convergence value, slow convergence speed, and local optimum due to the lack of the analysis of actual data when adopting M-H sampling based on uniform distribution in the priorinversion model updating, thereby minimizing the scope of convergence values, and being capable of quickly finding the optimum.

Description

technical field [0001] The invention relates to the fields of geophysical inversion and oil-gas reservoir prediction, in particular to an inversion model updating method based on Gaussian distribution M-H sampling. Background technique [0002] Seismic inversion is an important step in predicting oil and gas reservoirs. It establishes an optimization problem based on the seismic record data known to the detector, through the seismic record data and the mathematical model of the physical quantity to be obtained, and solves the optimization problem through the inversion method , so as to obtain the process of optimal estimation of the physical quantity to be sought. The seismic inversion technology based on Monte Carlo-Markov chain is an important method of seismic inversion. It updates the inversion model through random sampling and completes the entire inversion process through the Markov chain. [0003] M-H (Metropolis-Hastings) sampling is an important method of random sa...

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Problems solved by technology

[0004] The purpose of the present invention is: the present invention provides a method for updating an inversion model based on Gaussian distribution M-H sampling, which solves the problem that the existing update inversion model uses M-H sampling based on uniform distribution and lacks analysis of actual data, resulting in excessive convergence range , slow convergence, falling into local optimum

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