Integration of Criteria for Neural Network Inversion

Abstract

Summary Neural networks (NN) are widely used for solving various problems of geophysical data interpretation and processing. The application of the neural network approximation (NNA) method for solving inverse problems, including inverse multi-criteria problems of geophysics that are reduced to a nonlinear operator equation of the first kind (respectively, to a system of operator equations) is considered. The NNA method assumes the construction of an approximate inverse operator of the problem using neural network approximation designs (MLP networks) on the basis of a preliminary constructed set of reference solutions to direct and inverse problems. Techniques for estimating the practical ambiguity (error) of approximate solutions to inverse multicriteria problems are considered. Results of solving the inverse two-criteria 3D problem in combination with magnetometry are presented. It is shown that the NNA method allows one to stably solve nonlinear multicriteria inverse 3D problems with many desired parameters in real-time, with accuracy acceptable for practice. The experience of calculations shows that the error in solving the two-criteria problem is less than the one-criterion.