构建太阳物理与空间天气预测的标准化数据集，助力机器学习应用
SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
- 基于NASA SDO卫星数据，整合AIA与HMI多源影像
- 覆盖2010年5月至2024年7月一个完整太阳周期
- 提供包括耀斑预测、日冕场外推等6类任务基准
本文提出SuryaBench，一个高分辨率、面向机器学习的太阳物理学数据集，源自NASA太阳动力学观测台（SDO），专为推进太阳物理与空间天气预测中的机器学习应用而设计。数据涵盖大气成像仪（AIA）和日震与磁成像仪（HMI）的处理影像，时间跨度为2010年5月至2024年7月的一个完整太阳周期。为适配机器学习任务，数据已进行预处理，包括航天器滚转角校正、轨道调整、曝光归一化及退化补偿。同时提供辅助基准数据集，支持活跃区分割、活跃区涌现预测、日冕磁场外推、太阳耀斑预测、太阳极紫外谱预测及太阳风速度估计等核心任务。通过建立统一、标准化的数据集，本工作旨在促进基准测试、提升可复现性，并加速面向关键空间天气预测的AI模型研发，弥合太阳物理、机器学习与业务预报之间的鸿沟。
原文摘要 · Abstract (English)
This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar EUV spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.
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