STOCHASTIC INVERSION OF GEOPHYSICAL DATA FOR ESTIMATING EARTH MODEL PARAMETERS
First Claim
1. A computer implemented stochastic inversion method for estimating model parameters of an earth model of a subsurface geological volume of interest, the method comprising:
- a) acquiring at least one geophysical data set that samples a portion of the subsurface geological volume of interest, each geophysical data set defines an acquisition geometry of the subsurface geological volume of interest;
b) generating a specified number of boundary-based multi-dimensional models of the subsurface geological volume of interest, said models being defined by model parameters;
c) generating forward model responses of the models for each specified acquisition geometry;
d) generating a likelihood value of the forward model responses matching the geophysical data set for each specified acquisition geometry;
e) saving the model parameters as one element of a Markov Chain for each model;
f) testing for convergence of the Markov Chains;
g) updating the values of the model parameters for each model and repeating b) to f) in series or in parallel, until convergence is reached;
h) deriving probability density functions for each model parameter of the models which form the converged Markov Chains;
i) calculating the variances, means, modes, and medians from the probability density functions of each model parameter for each model to generate estimates of model parameter variances and model parameters for the earth models of the subsurface geological volume of interest which are utilized to determine characteristics of the subsurface geological volume of interest.
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Abstract
A computer implemented stochastic inversion method for estimating model parameters of an earth model. In an embodiment, the method utilizes a sampling-based stochastic technique to determine the probability density functions (PDF) of the model parameters that define a boundary-based multi-dimensional model of the subsurface. In some embodiments a sampling technique known as Markov Chain Monte Carlo (MCMC) is utilized. MCMC techniques fall into the class of “importance sampling” techniques, in which the posterior probability distribution is sampled in proportion to the model'"'"'s ability to fit or match the specified acquisition geometry. In another embodiment, the inversion includes the joint inversion of multiple geophysical data sets. Embodiments of the invention also relate to a computer system configured to perform a method for estimating model parameters for accurate interpretation of the earth'"'"'s subsurface.
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Citations
19 Claims
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1. A computer implemented stochastic inversion method for estimating model parameters of an earth model of a subsurface geological volume of interest, the method comprising:
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a) acquiring at least one geophysical data set that samples a portion of the subsurface geological volume of interest, each geophysical data set defines an acquisition geometry of the subsurface geological volume of interest; b) generating a specified number of boundary-based multi-dimensional models of the subsurface geological volume of interest, said models being defined by model parameters; c) generating forward model responses of the models for each specified acquisition geometry; d) generating a likelihood value of the forward model responses matching the geophysical data set for each specified acquisition geometry; e) saving the model parameters as one element of a Markov Chain for each model; f) testing for convergence of the Markov Chains; g) updating the values of the model parameters for each model and repeating b) to f) in series or in parallel, until convergence is reached; h) deriving probability density functions for each model parameter of the models which form the converged Markov Chains; i) calculating the variances, means, modes, and medians from the probability density functions of each model parameter for each model to generate estimates of model parameter variances and model parameters for the earth models of the subsurface geological volume of interest which are utilized to determine characteristics of the subsurface geological volume of interest. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A system configured to execute a computer readable medium containing a program which, when executed, performs an operation comprising:
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a) acquiring at least one geophysical data set that samples a portion of the subsurface geological volume of interest, each geophysical data set defines an acquisition geometry of the subsurface geological volume of interest; b) generating a specified number of boundary-based multi-dimensional models of the subsurface geological volume of interest, said models being defined by model parameters; c) generating forward model responses of the models for each specified acquisition geometry; d) generating a likelihood value of the forward model responses matching the geophysical data set for each specified acquisition geometry; e) saving the model parameters as one element of a Markov Chain for each model; f) testing for convergence of the Markov Chains; g) updating the values of the model parameters for each model and repeating b) to f) in series or in parallel, until convergence is reached; h) deriving probability density functions for each model parameter of the models which form the converged Markov Chains; i) calculating the variances, means, modes, and medians from the probability density functions of each model parameter for each model to generate estimates of model parameter variances and model parameters for the earth models of the subsurface geological volume of interest which are utilized to determine characteristics of the subsurface geological volume of interest. - View Dependent Claims (19)
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Specification