Application of constructive learning algorithms to the inverse problem

A constructive learning algorithm is used to generate networks that learn to approximate the functional of the magnetotelluric inverse problem. Based on synthetic data, several experiments are performed in order to generate and test the neural networks. Rather than producing, at the present time, a practical algorithm using this approach, the object of the paper is to explore the possibilities offered by the new tools. The generated networks can be used as an internal module in a more general inversion program, or their predicted models can be used by themselves or simply as inputs to an optimization program.