太阳光谱观测是研究太阳大气活动现象有效手段之一。 提出了一种基于主成分分析的太阳光谱特征信息提取和重构方法, 分析了重构数据噪声抑制程度和主成分阶数的关系, 计算了不同主成分阶数下重构数据的谱线信噪比以及多普勒速度测量精度。 结果显示特征信息提取后, 重构数据较大程度保留了原始光谱数据信息, 光谱数据信噪比明显提高, 谱线多普勒速度测量精度也显著提高, 并且三维光谱数据存储和传输量大幅缩减。 该方法能够满足一米新真空太阳望远镜当前数据规范发布需求和科学目标要求, 为中国在建的光纤阵列太阳望远镜以及未来的巨型太阳望远镜光谱数据处理提供参考。

Solar spectrum observation is one of the effective methods to study solar atmospheric phenomena. In this paper, a method of extracting and reconstructing solar spectral information based on principal component analysis (PCA) was proposed. Besides, the relation between the noise suppression degree of reconstructed data and the order of principal components was analyzed. In addition, the signal-to-noise ratio of the spectral line and the accuracy of the Doppler velocity measurement were calculated under different principal component orders. The results showed that after the feature information extraction, the reconstructed data greatly preserved the original spectral data, and their signal-to-noise was markedly improved, thus the Doppler velocity measurement accuracy of spectral line was significantly improved, and also the amount of data storage and transmission of the 3D spectral data were greatly reduced. This method can satisfy the releasing requirements of current data standard and scientific goals of the 1-meter New Vacuum Solar Telescope. This method also provide a reference for the spectral data processing of the under construction Fiber Arrayed Solar Optical Telescope and future Chinese Giant Solar Telescope.