高光谱技术快速、 无损、 精确探测矿物, 能够清楚的反映矿物化学成分的改变。 石榴子石在热红外波段具有诊断性的三峰式特征。 反射峰波长与化学成分关系密切, 所以可以依据石榴子石在热红外波段的光谱特征开展其亚类分类研究。 钙铬榴石和锰铝榴石反射峰位置易于与其他亚类区分, 而铁铝榴石和镁铝榴石、 钙铁榴石和钙铝榴石的反射峰位置有较大重叠区域, 无法直接判别, 因此亟需一种基于热红外光谱的快速、 准确识别石榴子石亚类的分类方法。 基于热红外光谱库中85个不同类型的石榴子石样本数据获取其3个反射峰位置及波长差值信息, 利用非线性BP神经网络、 聚类分析以及多元线性判别分析3种方法开展石榴子石亚类识别实验, 并运用精确率、 召回率和F1值进行分类精度评价。 结果显示: BP神经网络算法分类的精确率、 召回率和F1值均能达到100%, 铁铝榴石和镁铝榴石、 钙铁榴石和钙铝榴石得到很好地区分; 聚类分析和多元线性判别分析分类的精确率、 召回率和F1值分别为86.1%、 80%和79.2%, 84.2%、 80%和79.5%, 这两种方法对反射峰重叠的铁铝榴石和镁铝榴石、 钙铁榴石和钙铝榴石分类效果不好, 因此BP神经网络更适合石榴子石亚类识别。 本研究利用BP神经网络强大的非线性自动映射能力, 找到了石榴子石热红外谱段反射峰位置与亚类类型之间复杂的映射关系, 证明了BP神经网络方法与热红外光谱特征结合使用的可行性与优越性, 为石榴子石亚类识别提供了快速有效的技术支撑, 同时为其他矿物的快速有效识别提供了良好的技术启示。

Hyperspectral technology is a rapid, nondestructive and accurate means of mineral detection, which can clearly reflect mineral chemical composition change. Garnets have the characteristic of three diagnostic peaks in the thermal infrared wavebands, and the wavelength positions of the reflection peaks are closely related to the chemical composition, so the subclass classification of garnets can be studied according to the thermal infrared wave spectrum characteristics. Reflection peak wavelength positions of uvarovite and spessartine are easy to distinguish with other types. However, that of almandine and pyrope, andradite and grossular have a large overlap and are difficult to distinguish with each other. Therefore, a fast and accurate classification method based on the thermal infrared spectrum is urgently needed. In this paper, the information about wavelength position and difference between wavelengths of the three reflection peaks of 85 different types of garnet samples were obtained from the thermal infrared spectroscopy library. Three nonlinear BP neural network methods, cluster analysis and multiple linear discrimination analysis were used to carry out garnet subclass recognition experiments, and the accuracy rate, recall rate and F1 value were used to evaluate the classification accuracy. The experimental results showed that the accuracy rate, recall rate and F1 value of BP neural network algorithm after classification could all reach 100%, and all types of garnets got a good distinction; the accuracy rate, recall rate and F1 value of clustering analysis and multivariate linear discriminant analysis were 86.1%, 80%, 79.2% and 84.2%, 80%, 79.5% separately, the four types of garnets with overlapping reflection peaks could not be well differentiated. According to the results, the nonlinear BP neural network is more suitable for the subclassification of garnets. Our study used powerful automatic nonlinear mapping ability of the BP neural network, has found the complex mapping relationship between the wavelength positions of the reflection peak in the thermal infrared spectrum of the garnets and the subclass types, and proved the feasibility and superiority of BP neural network method combined with thermal infrared spectrum characteristics. The identification of garnet subclass provided is fast and effective, and it can give good technical enlightenment for the rapid and effective identification of other minerals.