Trans-dimensional Monte Carlo sampling applied to the magnetotelluric inverse problem

3rd International Conference on Mathematical Modeling in Physical Sciences

物理学;数学

Mandolesi, Eric^1 ; Agostinetti, Nicola Piana^1

Dublin Institute for Advanced Studies, 5 Merrion Square, Dublin 2, Ireland^1

关键词: Bayesian frameworks;    Electrical conductivity;    Geological structures;    Geophysical methods;    Monte Carlo sampling;    Monte carlo sampling algorithms;    Objective functions;    Regularization terms;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/574/1/012132/pdf
DOI  :  10.1088/1742-6596/574/1/012132

来源: IOP

【 摘 要 】

The data required to build geological models of the subsurface are often unavailable from direct measurements or well logs. In order to image the subsurface geological structures several geophysical methods have been developed. The magnetotelluric (MT) method uses natural, time-varying electromagnetic (EM) fields as its source to measure the EM impedance of the subsurface. The interpretation of these data is routinely undertaken by solving inverse problems to produce 1D, 2D or 3D electrical conductivity models of the subsurface. In classical MT inverse problems the investigated models are parametrized using a fixed number of unknowns (i.e. fixed number of layers in a 1D model, or a fixed number of cells in a 2D model), and the non-uniqueness of the solution is handled by a regularization term added to the objective function. This study presents a different approach to the 1D MT inverse problem, by using a trans-dimensional Monte Carlo sampling algorithm, where trans-dimensionality implies that the number of unknown parameters is a parameter itself. This construction has been shown to have a built-in Occam razor, so that the regularization term is not required to produce a simple model. The influences of subjective choices in the interpretation process can therefore be sensibly reduced. The inverse problem is solved within a Bayesian framework, where posterior probability distribution of the investigated parameters are sought, rather than a single best-fit model, and uncertainties on the model parameters, and their correlation, can be easily measured.