Artificial Neural Network for Downward Continuation of Anomalous Magnetic Fields

The downward continuation of an anomalous magnetic field is used for many applications in geophysics. However, such a problem is ill-posed, so it does not have a unique and stable solution. In this paper, we propose an artificial neural network architecture for the downward continuation of the vertical component of an anomalous geomagnetic field measured in a plane at a given height. The inverse problem is solved here by a direct method: the neural network is trained to reconstruct such a distribution of the magnetic field Bdown, which after a stable upward continuation corresponds to the measured field Bup. The performance of the neural network was demonstrated using the example of an anomalous geomagnetic field obtained using the Enhanced Magnetic Model.