A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting

• URL: http://arxiv.org/abs/2204.05780v1
• Date: Mon, 4 Apr 2022 15:38:33 GMT
• Title: A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting
• Authors: Kyle Domico, Ryan Sheatsley, Yohan Beugin, Quinn Burke and Patrick McDaniel
• Abstract summary: Geomagnetic storms are disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun. Current prediction methods at NOAA are limited in that they depend on expensive solar wind spacecraft and a global-scale magnetometer sensor network. We introduce a novel machine learning and computer vision approach to accurately forecast geomagnetic storms without the need of such costly physical measurements.
• Score: 2.0499240875882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notably, they have the potential to damage satellites and cause instability in power grids on Earth, among other disasters. They result from high sun activity, which are induced from cool areas on the Sun known as sunspots. Forecasting the storms to prevent disasters requires an understanding of how and when they will occur. However, current prediction methods at the National Oceanic and Atmospheric Administration (NOAA) are limited in that they depend on expensive solar wind spacecraft and a global-scale magnetometer sensor network. In this paper, we introduce a novel machine learning and computer vision approach to accurately forecast geomagnetic storms without the need of such costly physical measurements. Our approach extracts features from images of the Sun to establish correlations between sunspots and geomagnetic storm classification and is competitive with NOAA's predictions. Indeed, our prediction achieves a 76% storm classification accuracy. This paper serves as an existence proof that machine learning and computer vision techniques provide an effective means for augmenting and improving existing geomagnetic storm forecasting methods.

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