岩石光谱综合反映了岩石的物理化学性质、 成分及其结构构造。 岩石光谱数据已被应用于岩石分类的研究, 但是不同于矿物光谱, 岩石光谱并无标准数据库, 且受较多干扰因素影响, 例如矿物组分、 结构构造、 化学成分、 风化力度, 测量仪器的误差等。 传统岩石光谱分类模型先是对岩石光谱进行预处理排除干扰, 然后采用不同方法对部分光谱特征分析, 以达到分类目的。 但对光谱数据特征遗失较多, 使得分类准确率低下且操作过程繁琐、 效率不高。 因此, 建立一个简单、 快速、 准确的岩石光谱自动分类模型具有重要意义。 机器学习能够对获得的所有数据进行学习, 不存在遗漏, 大大提高了分类精度, 且是对原始数据直接操作, 不需预处理, 简化流程。 为此, 选取辽宁兴城地区作为研究区, 采集了若干种典型岩石样本, 利用美国ASD便携式光谱仪实测光谱, 最终获得608条数据, 依据岩石光谱特征分为三类进行研究。 首先利用决策树(DT)及决策树的升级模型——随机森林(RF)对数据进行分类, 但当数据噪音较大时随机森林容易陷入过拟合; 因而利用对异常值不敏感的K-最近邻(KNN)建模, 但KNN需要对每个样本都考虑, 数据量大时计算量会很大, 效率不高; 所以通过支持向量机(SVM)来提升分类准确率。 从实验结果可以看出, 4种分类模型的准确率排序为: SVM>KNN>RF>DT。 为进一步提高岩石光谱特征的自动分类精度, 采取了融合多个不同模型的办法, 即对不同模型的分类结果进行投票, 选择投票最多的作为最后分类结果。 由于硬投票可在一定程度上减少过拟合现象的发生, 更加适合分类模型, 所以利用硬投票法融合了RF、 KNN与SVM三个机器学习模型, 最终的分类准确率可达到99.17%。 综上所述, 基于融合学习模型进行岩石光谱特征自动分类是切实可行且准确高效的。

The spectrum of rock is a comprehensive reflection of the physical chemistry properties, composition and structure of the rock. Rock spectral data have been applied to the study of rock classification. But unlike the mineral spectrum, the Rock spectrum has no standard database and is influenced by many disturbing factors, for example, mineral composition, structure, chemical composition, weathering strength, the error of measuring instrument, etc. The traditional rock spectrum classification model firstly preprocesses the rock spectrum to eliminate the interference. Then, some spectral features are analyzed by different methods to achieve the classification goal. However, the loss of spectral data features makes the classification of low accuracy and cumbersome operation process; efficiency is not high. Therefore, it is of great significance to establish a simple, fast and accurate automatic classification model of the rock spectrum. Machine learning can learn all the data obtained; there is no omission, greatly improving the classification accuracy. And is the direct operation to the original data, does not need the pretreatment, simplifies the process. Therefore, Xingcheng city of Liaoning Province, China was chosen as the study area, and several typical rock samples were collected. Based on the measured spectral data from the ASD Portable Spectrometer, 608 pieces of data were obtained. According to the spectral characteristics of rocks, the study is divided into three types. Firstly, the decision tree and the upgrade model of the decision tree are used as a the random forest, But when the data noise is large, random forest is easy to get into overfitting. Therefore, the knearest neighbor model, which is not sensitive to outlier is used. But KNN needs to consider every sample when the data is large, the computation will be very large, inefficient. So use Support vector machine to improve classification accuracy. The experimental results show that the order of accuracy of the four classification models is: SVM>KNN>Random Forest>Decision Tree. In order to further improve the automatic classification accuracy of rock spectral features. By fusing several different models. That is to vote on the classification results of different models, choose the most votes as the final classification results. Since hard voting can reduce the occurrence of over-fitting to a certain extent, it is more suitable for classification models. In this paper, we use a hard voting method to fuse three machine learning models: RF, KNN and SVM. The final classification accuracy can reach 99.17%. To sum up, it is feasible, accurate and efficient to classify rock spectral features automatically based on the fusion learning model.