深度学习在识别地震前磁场模式中的应用和解释
Application and interpretation of deep learning for identifying pre-emergence magnetic-field patterns
摘要：
在太阳内部产生的磁通量出现在表面，形成活动区（AR）和太阳黑子。通量浮现可能引发爆炸性事件，如耀斑和日冕物质抛射，因此了解浮现对空间天气预报是有用的。任何出现前的签名的证据也将阐明负责出现的地下过程。在本文中，我们使用深度卷积神经网络（CNN）首次分析了来自太阳动力学观测台/日震新兴活动区（SDO/HEAR）数据集（Schunker et al. 2016）的新兴AR，以表征出现前的表面磁场特性。训练的CNN在出现前（PE）视线磁图和非出现（NE）磁图的对照组之间进行分类，其中真实技能统计（TSS）得分为约85%，在出现前3小时和约40%，在出现前24小时。我们的结果是优于基线分类TSS得到的判别分析，只有无符号的磁通量。我们开发了一种网络修剪算法来解释训练的CNN，并表明CNN采用了积极响应的过滤器，以及消极的无符号的磁通量的磁图。使用合成磁图，我们证明了CNN输出对磁性区域的长度尺度是敏感的，小尺度和强磁场产生最大的CNN输出和可能的特征出现前模式。鉴于深度学习的日益普及，这里开发的用于解释训练后的CNN的技术-使用网络修剪和合成数据-与太阳和天体物理数据分析的未来应用相关。
英文摘要：
Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events, such as flares and coronal mass ejections and therefore understanding emergence is useful for space-weather forecasting. Evidence of any pre-emergence signatures will also shed light on sub-surface processes responsible for emergence. In this paper, we present a first analysis of emerging ARs from the Solar Dynamics Observatory/Helioseismic Emerging Active Regions (SDO/HEAR) dataset (Schunker et al. 2016) using deep convolutional neural networks (CNN) to characterize pre-emergence surface magnetic-field properties. The trained CNN classifies between pre-emergence (PE) line-of-sight magnetograms and a control set of non-emergence (NE) magnetograms with a True Skill Statistic (TSS) score of ~85%, 3h prior to emergence and ~40\%, 24h prior to emergence. Our results are better than a baseline classification TSS obtained using discriminant analysis of only the unsigned magnetic flux. We develop a network pruning algorithm to interpret the trained CNN and show that the CNN incorporates filters that respond positively as well as negatively to the unsigned magnetic flux of the magnetograms. Using synthetic magnetograms, we demonstrate that the CNN output is sensitive to the length-scale of the magnetic regions with small-scale and intense fields producing maximum CNN output and possibly a characteristic pre-emergence pattern. Given increasing popularity of deep learning, techniques developed here for interpretation of the trained CNN -- using network pruning and synthetic data -- are relevant for future applications in solar and astrophysical data analysis.
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