Abstract
Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often lack representation learning from solar images. On the other hand, end-to-end learning approaches struggle to model long-range temporal dependencies in solar images.
In this study, we propose Deep Space Weather Model (Deep SWM), which is based on multiple deep state space models for handling both ten-channel solar images and long-range spatio-temporal dependencies. Deep SWM also features a sparse masked autoencoder, a novel pretraining strategy that employs a two-phase masking approach to preserve crucial regions such as sunspots while compressing spatial information.
Furthermore, we built FlareBench, a new public benchmark for solar flare prediction covering a full 11-year solar activity cycle, to validate our method.
Our method outperformed baseline methods and even human expert performance on standard metrics in terms of performance and reliability.
Multi-Wavelength Solar Observations
Exploring the Sun's dynamic nature through different wavelengths, revealing the complex interactions between plasma, magnetic fields, and solar phenomena
Data sources: AIA level 1 images in nine wavelengths (EUV: 94Å, 131Å, 171Å, 193Å, 211Å, 304Å, 335Å; UV: 1600Å; Visible: 4500Å) and high-resolution (1K) magnetograms from the HMI, obtained from JSOC.
Overview
Problem Setting
We tackle the challenging task of predicting the class of the largest solar flare within a 24-hour horizon using full-disk multi-wavelength solar images. This is formulated as a multi-class classification problem with significant real-world implications for space weather forecasting.
Correspondence between flare classes and peak X-ray flux intensities.
Model Architecture
We propose Deep SWM, a novel architecture extending deep state-space models for classifying the maximum solar flare class within a 24-hour horizon, utilizing HMI and multi-wavelength AIA images.
The novelties of our proposed method are as follows:
Solar Spatial Encoder (SSE)
Comprising the Depth-wise Channel Selective Module (DCSM) and the Spatio-Temporal State-Space Module (ST-SSM). The DCSM selectively weights multi-wavelength image channels to emphasize features relevant to solar events, while the ST-SSM efficiently captures long-range spatio-temporal dependencies in the solar images.
Long-range Temporal SSM (LT-SSM)
Extends deep state-space models to effectively model temporal dependencies exceeding the solar rotation period within the intermediate features obtained from the pretraining stage. This allows the LT-SSM to efficiently capture long-range relationships that are crucial for solar flare prediction.
Sparse MAE
A pretraining strategy tailored for solar images that extends the Masked Autoencoder (MAE). Sparse MAE addresses the challenge of sparse, yet crucial, information regions in solar images (e.g., sunspots) using a novel two-phase masking approach. This ensures that these crucial regions are less likely to be completely masked during pretraining, leading to improved intermediate feature representations.
Quantitative Results
Our method outperforms all baseline approaches across all metrics and even surpasses human expert performance, demonstrating the effectiveness of our approach for solar flare prediction.
Table 1: Comparison of our method with state-of-the-art approaches and human experts. Higher values are better for all metrics.
Qualitative Results
BibTeX
@inproceedings{nagashima2025deepswm,
title={Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images},
author={Shunya Nagashima and Komei Sugiura},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2025}
}