Physics-informed Neural Networks for the Improvement of Platform Magnetometer Measurements

Abstract

Space-based measurements of the Earth’s magnetic field with a good spatiotemporal coverage are needed to understand the complex system of our surrounding geomagnetic field. High-precision magnetic field satellite missions form the backbone for sophisticated research, but they are limited in their coverage. Many satellites carry so-called platform magnetometers that are part of their attitude and orbit control systems. These can be re-calibrated by considering different behaviors of the satellite system, hence reducing their relatively high initial noise originating from their rough calibration. These platform magnetometer data obtained from non-dedicated satellite missions complement the high-precision data by additional coverage in space, time, and magnetic local times. In this work, we present an extension to our previous Machine Learning approach for the automatic in-situ calibration of platform magnetometers. We introduce a new physics-informed layer incorporating the Biot-Savart formula for dipoles that can efficiently correct artificial disturbances due to electric current-induced magnetic fields evoked by the satellite itself. We demonstrate how magnetic dipoles can be co-estimated in a neural network for the calibration of platform magnetometers and thus enhance the Machine Learning-based approach to follow known physical principles. Here we describe the derivation and assessment of re-calibrated datasets for two satellite missions, GOCE and GRACE-FO, which are made publicly available. We achieved a mean residual of about 7 nT and 4 nT for low- and mid-latitudes, respectively.