Rapid neuronet inversion of 2D magnetotelluric data for monitoring of geoelectrical section parameters

The inverse MagnetoTelluric (MT) operator is approximated by means of the Neural Network (NN). The methodology of the NN interpretation in classes of the geoelectrical sections described by the hundreds of parameters is proposed. Error of the NN inversion and field misfit are evaluated. A rapid NN algorithm solving the inverse problem and detecting changes of time-dependent dynamic parameters of the section is applied to 2D synthetic data

Rapid neuronet inversion of 2D magnetotelluric data for monitoring of geoelectrical section parameters

The inverse MagnetoTelluric (MT) operator is approximated by means of the Neural Network (NN). The methodology of the NN interpretation in classes of the geoelectrical sections described by the hundreds of parameters is proposed. Error of the NN inversion and field misfit are evaluated. A rapid NN algorithm solving the inverse problem and detecting changes of time-dependent dynamic parameters of the section is applied to 2D synthetic data

Methodology for Solving High-dimensional Multi-Parameter Inverse Problems of Indirect Measurements

Inverse problems (IP) of indirect measurements are a class of IP encountered in most modern nature science experiments. Unfortunately, they are characterized by a number of properties making them hard to solve: they may be ill-posed or even incorrect, non-linear, and often they are characterized by high dimension by input and/or by output. As such, IP of indirect measurements require special methods to solve them. One of the classes of such methods are methods of machine learning (ML), which however possess special properties which should be taken into account when using them. In this paper, the authors suggest an outline of a special methodology, which can become the base for a standard scenario for processing data of indirect measurement IP with ML methods. The main notions underlying this methodology are also described and explained