Modeling and forecasting ground geomagnetic perturbations using deep learning on spherical harmonics
Channel: COSPAR2021 Workshops
Date: 02/02/2021

Authors: Banafsheh Ferdousi, Panagiotis Tigas, Vishal Upendran, Teo Bloch, Mark CM Cheung, Siddha Ganju, Asti Bhatt, Ryan McGranaghan, Yarin Gal

Abstract: Ground magnetic perturbations have a strong contribution in formation of geomagnetic induced currents (GICs) that could impose hazards to our infrastructures like power grids, oil pipelines, railways and telecommunication systems. Accurate estimation of GICs are required for reliable space weather prediction capability and for mitigation strategy on the Earth. Current forecasting models are either computationally expensive like magnetohydrodynamic (MHD) models and do not capture important small scale structure measured on the ground, or use averaging geomagnetic and solar wind measurements like empirical models that do not provide a dynamic forecast and only produces a statistical representation of behaviour.

In this study, we created high-resolution (spatial and temporal) global models of ground geomagnetic perturbation by applying a compressed sensing technique to decompose global geomagnetic variations in terms of Spherical Harmonics down to 1 min cadence based on sample measurements from the SuperMAG network. The model is evaluated by sampling ground perturbation from MHD simulations at SuperMAG locations, and comparing the reconstruction with the ground truth MHD data. We also developed a forecasting model in Spherical Harmonics using solar wind measurements from Lagrangian point 1 (from the OMNI database) to predict global magnetic perturbation on the ground by using a Deep Neural Network. The model performance was evaluated and has improved the performance in the Wiemer, 2013 model by 14% and MHD simulation by 24%.