MAGInet based on deep learning for magnetic multi-parameter inversion

This manuscript introduces MAGInet, a novel deep learning framework designed for the magnetic multi-parameter inversion of complex structures. The architecture of MAGInet integrates a classifier and several solvers, where the classifier performs a preliminary categorization of magnetic field signals and the solvers execute a detailed regression analysis to predict multiple parameters of the structures. The operational sequence of MAGInet is as follows: magnetic field signals are initially fed into the classifier for classification, and the outcomes are subsequently channeled into the appropriate solver for multi-parameter forecasting. The underlying principle of MAGInet’s multi-parameter inversion is to establish a learned mapping between the magnetic field signals and the multi-magnetic parameters of complex structures, leveraging extensive training datasets. In this study, simulation experiments are conducted, with training datasets generated via finite element magnetic field modeling, which are then utilized to fine-tune the MAGInet model. The results of these experiments demonstrate that the accuracy of multi-parameter prediction for complex structures can reach up to 97.5% under zero-error conditions. Furthermore, the efficacy of the MAGInet inversion approach is contrasted with the traditional, deep, fully connected neural network methodologies. Comparative analyses reveal that MAGInet significantly outperforms traditional deep neural networks in terms of accuracy for predicting the magnetic multi-parameters of complex structures, showcasing superior performance.