Neural network inversion of data in classes of parametrized geoelectric sections

A method of approximate magnetotelluric sounding (MTS) data inversion is developed on the basis of the representation of the inverse operator by an artificial neural network in classes of geoelectric structures. A methodology of the neural network inversion of magnetotelluric data is proposed for a family of classes of geoelectric structures and the uncertainty of the inferred results is estimated. A neural network algorithm of MTS data inversion is tested using synthetic 2-D data.