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题名: 基于自动特征提取方法的太阳耀斑预报模型
其他题名: Solar flare forecasting model based on automatic feature extraction
作者: 李蓉; 朱杰; 黄鑫; 崔延美
作者部门: 空间环境部
关键词: 太阳耀斑 ; 太阳活动区 ; 磁场特征 ; 机器学习
刊名: 科学通报
ISSN号: 0023-074X
出版日期: 2016
卷号: 61, 期号:36, 页码:3958-3963
收录类别: CSCD
项目资助者: 国家自然科学基金 ; 智能物流系统北京市重点实验室项目 ; 北京市智能物流系统协同创新中心项目资助
中文摘要: 在太阳耀斑预报模型中,首先需要从原始观测数据中提取刻画太阳活动区特性的物理特征参量,然后使用统计或机器学习方法寻找物理特征参量与太阳耀斑发生的关系,以达到建立太阳耀斑预报模型的目的.其中,太阳活动区物理特征的提取在整个建模过程中发挥着重要的作用,活动区物理特征的优劣直接决定着预报模型性能的高低.然而,随着机器学习技术的发展,机器学习方法中的深度学习算法能够从原始数据中自动提取特征,并建立预报模型.本文利用深度学习方法建立了一个太阳耀斑预报模型.与先提取活动区物理参量、再建立预报模型的传统机器学习方法相比较,本文所建立的预报模型具有更好的预报性能.
英文摘要: Solar flares are outbursts in the solar atmosphere resulting from sudden release of magnetic energy. The associated high energy particles and radiation threaten the safety of astronauts, reduce the lifetime of satellites, disturb the radio communications and degrade the precision of Global Positioning System. The radiation reaches the Earth about 8 min, and high energy particles take about 30 min to reach the earth after a solar flare. So solar flare forecasting is critical for providing enough time to respond to the space weather effects. Up to now, many statistical and machine learning methods are used to build a solar flare forecasting model. A machine learning based solar flare forecasting model normally requires solar physicists to design a feature extractor which can transform the observational images of active regions into physical features, and then the relationships between the features and the solar flares are discovered by the machine leaning algorithm. The priori knowledge of the solar physicists is added into the solar flare forecasting model by designing the feature extractor. For most of the machine learning methods, the hard part is what kinds of features should be extracted from the raw data. Considerable solar physicists spend a lot of time extracting the physical parameters from observational data of active regions. Deep learning method, which removes this manual step, can automatically discover useful patterns from the raw data and build a forecasting model. Instead of designing the feature extractor by solar physicists, we learn a solar flare forecasting model from magnetogram pixels by using deep learning method. We use Caffe, which is a deep learning framework developed by the Berkeley Vision and Learning Center, to build a convolutional neural network for solar flare forecasting. In order to compare the performance of proposed forecasting model with that of the forecasting model built by using traditional machine learning method, we build the other solar flare forecasting model based on the same dataset. In the traditional forecasting model, physical parameters designed by the solar physicist are extracted from the magnetogram of active regions, and then these parameters are fed to the forecasting model. We build a traditional forecasting model by multilayer neural networks. For convenience, the solar flare forecasting model built by using deep learning method is called deep model, and the solar flare forecasting model built by using the traditional multilayer neutral networks is called traditional model. Using the same testing data, the performances of the deep model and the traditional model are evaluated and compared. We find that the performance of the deep model is little better than that of the traditional model. The results confirm that the deep model can automatically learn solar flare forecasting features from magnetograms of active regions. This is our first time to automatically learn the forecasting patterns for solar flares from raw data instead of designing the physical patterns by solar physicists. The effectiveness of the deep learning method for the solar flare forecasting is validated. In the future, the deep learning method can be used to automatically discover the solar flare forecasting patterns from the vector magnetograms or the extreme ultraviolet images of active regions.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.nssc.ac.cn/handle/122/5740
Appears in Collections:空间环境部_期刊论文

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