| Abstract: | Hopfield neural networks are massive parallel automata that support specific models and are adept in solving optimization problems. They suffer from a ‘rough’ solution space and convergence properties that are highly dependent on the starting model or prior. These detractions may be overcome by introducing regularization into the network in the form of local feedback smoothing. Application of regularized Hopfield networks to over 50 optimization test cases has yielded successful results, even with uniform (minimal information) priors. In particular, the non-linear, one- and two-dimensional magnetotelluric inverse problems have been solved by means of these regularized networks. The solutions compare favourably with those produced by other methods. Such regularized networks, with either hardware or programmed, parallel-computer implementation, can be extended to the problem of three-dimensional magnetotelluric inversion. Because neural networks are natural analog-to-digital converters, it is predicted that they will be the basic building blocks of future magnetotelluric instrumentation. |
