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This paper studies the application condition of linearized model uncertainty estimate numerically for the Magnetotelluric (MT) problem using Bayesian inference theory, based on a three layer case with different parameter combinations. The nonlinear uncertainty estimate is calculated with relatively intensive computation, via Markov chain Monte Carlo method of Metropolis Hastings, giving an unbiased...
The total mean-square error (MSE) of the estimated model, defined as the sum of the standard model variance and the bias variance, is used to define the truncation level of the singular-value decomposition to give a reasonable balance between model resolution and model variance. This balance is determined largely by the data and no further assumptions are necessary except that the bias terms are estimated...
Genetic algorithm, one of the new methods for global non-linear optimization problem, has been applied in magnetotelluric data analysis. In this paper, the magnetotelluric inverse problem was studied by a hybrid genetic algorithm, which was based on the combination of simplex method and genetic algorithm. The standard genetic algorithm has poor local search ability, large amounts of calculation, and...
Geophysics' complexity and uncertainty, and interfering with a variety of humanities, which made the gaining first-hand information from the field in the process of data acquisition is often severely disrupted. The solution in the geophysical inversion would be many solutions or distortion, which is the geophysical ill-posed problem. Ill-posed problems could be attributed to the First Class of Operator...
The two-dimensional magnetotelluric inverse problem is ill-posed and the inverse results are unstable and non-unique. It means that different geo-electrical model could fit the observed data with the same accuracy. A stable solution of the ill-posed inverse problem can be obtained by utilizing the regularization methods in the objective function. Solving large scale linear equation of inverse problem,...
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