Application of Artificial Neural Networks for Reconstruction of Vector Magnetic Field from Single-Component Data

Abstract

In this work the problem of reconstructing the vector anomalous magnetic field from single-component data was solved by means of artificial neural networks. For training an artificial neural network a database of anomalous magnetic field components \({{B}_{x}}\), \({{B}_{y}}\), \({{B}_{z}}\) was created using a set of point magnetic dipoles lying under the field measurement plane. Using a synthetic example, the work of a trained neural network was shown in comparison with a well-known numerical algorithm for restoring a vector field from data of one component. Further, according to the data of the vertical component of the anomalous geomagnetic field the horizontal components of the anomalous geomagnetic field were restored using artificial neural networks in the territory of 58°–85° E, 52°–74° N with a grid step of 2 arc minutes.