# Global geomagnetic perturbation forecasting using Deep Learning

arXiv: https://arxiv.org/abs/2205.12734 · HTML: https://mlsanity.com/paper/2205.12734/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 76 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA](https://mlsanity.com/paper/2012.10610/) (2020)
- [Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder](https://mlsanity.com/paper/1910.03085/) (2019)
- [A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning](https://mlsanity.com/paper/1912.01038/) (2019)

## Built on by (most cited first, 4 of 4)

- [Surya: Foundation Model for Heliophysics](https://mlsanity.com/paper/2508.14112/) (2025)
- [Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting](https://mlsanity.com/paper/2402.17570/) (2024)
- [The geomagnetic storm and Kp prediction using Wasserstein transformer](https://mlsanity.com/paper/2503.23102/) (2025)
- [Wearable environmental sensing to forecast how legged systems will interact with upcoming terrain](https://mlsanity.com/paper/2602.09209/) (2026)

## Abstract (arXiv)

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 minutes into the future using only solar wind measurements as input. The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1-minute cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.