A neural approach to the integrated inversion of geophysical data of different types

Artificial neural networks (ANNs) have been employed for the inversion of the geometrical parameters of a magma-filled dike, which causes observable changes in various geophysical fields. The inversion approach, which is based on the function approximation capabilities of multilayer perceptrons (MLPs), is also carried out by a systematic search technique based on the simulated annealing (SA) optimization algorithm in order to emphasize the merits of the proposed strategy. It is shown that even if the SA approach guarantees a high degree of accuracy, it requires a considerable amount of time, incompatible with on-line applications. On the other hand, it is shown that MLPs, once correctly trained, can solve the inversion problem very fast and with an appreciable degree of accuracy. It is also demonstrated that an integrated approach involving geophysical data of different kinds allows for a more accurate solution than when ground deformation data alone is considered. The results given in the paper are supported by experiments carried out using an interactive software tool developed ad hoc, which allows both direct and inverse modeling of data related to the opening of a crack at the beginning and throughout a volcanic activity episode