An approximation method for solving the inverse MTS problem with the use of neural networks |
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Authors: | M I Shimelevich E A Obornev |
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Institution: | 1.Moscow State Geological Prospecting University,Moscow,Russia |
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Abstract: | The work develops the approximation approach to solving the inverse MTS problem with the use of neural networks. The inverse
problem is considered in model classes of parametrized geoelectric structures, whose electric conductivity is controlled by
a few hundreds of macroparameters (N ∼ 300). An approximate inverse operator of the problem is constructed for each model class as a neural network, whose coefficients
are determined in the process of training on a representative sample of standard examples of forward problem solutions. The
problem of determination of the model class of geolectric structures corresponding to the presented input MT data is solved
with the use of the neural network classifier constructed for the available set of model classes of structures. Regularizing
factors and errors of the neural network method are analyzed. The operation of the algorithm is illustrated by examples of
the 2-D inversion of synthetic MT data. |
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