岩石含水量是影响岩石物理、 化学和力学特性的一个重要指标。 在岩土工程、 隧道工程等领域, 岩石含水量的大小是诱发灾变和病害的关键原因。 与传统方法相比, 利用近红外光谱(NIRS)特征检测岩石含水量, 具有无损、 定量的明显优势, 其难点和关键是近红外光谱的特征选择。 针对该问题, 进行了室内实验, 研究不同含水量下的岩石近红外光谱的特征选择。 特征选择方法中的Filter法, 利用样本数据内在的特点, 评价特征的重要程度, 增强了特征与类的相关性, 同时削减了特征之间的相关性, 具有复杂度低、 直观、 效率高、 普适性强的优点, 符合该研究的数据特点。 因此, 选用Filter型的依赖性度量法进行特征选择。 室内实验中, 首先制备11种不同含水量的砂岩试样, 并分别采集了前后左右4个测试点处的共计44条近红外光谱曲线; 然后, 利用一阶导数法对光谱进行预处理, 基于此, 选择1 400和1 930 nm谱段进行光谱特征分析, 并分别提取2个谱段处的峰面积、 峰高、 半高宽、 左肩宽度、 右肩宽度、 左右肩宽比共计6个初始特征变量; 考虑到6个初始特征变量的量纲不同, 且变量之间的变化幅度不同, 对原始数据进行正规化变换, 消除量纲和变化幅度不同带来的影响; 接着, 根据自变量的筛选原则, 去掉自变量之间具有强线性相关的冗余变量; 然后, 利用依赖性度量法中的统计相关系数作为相关程度的度量标准, 分析了初始特征变量之间以及初始特征变量与含水量之间的相关程度, 并得到了2个强相关谱段处的最优特征变量; 最后, 在强相关谱段处分别构建了多元回归模型, 并对模型进行了检验分析。 研究结果表明: (1)波长1 400和1 930 nm附近的近红外光谱吸收峰特征与岩石含水量有明显相关性; (2)波长1 400 nm处的峰高、 右肩宽度、 左肩宽度与含水量线性相关性明显; 波长1 930 nm处的峰高、 右肩宽度与含水量线性相关性明显; (3)多元线性回归模型能够较精确表达含水量与近红外光谱之间的相关性, 利用该模型可实现基于近红外光谱特征的含水岩石含水量预测, 为利用近红外光谱实现动态监测与评估岩石含水量提供基础建模数据。

Water content of rock is an important index to affect the physical, chemical and mechanical properties of rock. In geotechnical engineering, tunnel engineering and other fields, water content is the key factor to induce disaster and disease. Compared with the traditional method, the determination of rock water content by using the feature of NIR spectrum (NIRS) has obvious advantages of nondestructive and quantitative analysis, and the difficulty and key is the feature selection of NIR spectrum. In order to solve this problem, laboratory experiments were carried out to study the feature selection of near infrared spectra of rock under different water content. The Filter method of feature selection, using the inherent characteristics of the sample data, evaluates the importance of the feature, enhances the correlation between the feature and the class, and reduces the correlation between the features, so it has the advantages of low complexity, being intuitionistic, high efficiency and strong universality and accords with the characteristics of the data studied in this paper. Therefore, this paper selects the Filter type dependency metric for feature selection. In the laboratory experiment, 11 kinds of sandstone samples with different moisture content were prepared, and 44 NIR spectra were collected respectively at 4 test points on the front, behind, left and right sides. Then, the first derivative method was used to preprocess the spectrum. Based on this, the spectral characteristics were analyzed at 1 400 and 1 930 nm, and six initial characteristic variables (the peak area, peak height, width of half height, width of left shoulder, width of right shoulder, the ratio of the width of the left shoulder to the width of the right shoulder )were extracted respectively. Considering the different dimensions and variation range of the six initial characteristic variables, the original data were normalized to eliminate the influence of different dimensions and variation ranges. And then, according to the principle of independent variable selection, redundant variables with strong linear correlation between independent variables were removed. Then, used the statistical correlation coefficient in the dependency metric as the measure of correlation degree, and the correlation among the initial characteristic variables and the correlation between the initial characteristic variables and water content were analyzed. The optimal characteristic variables at two strongly correlated spectral segments were obtained. Finally, multiple regression models were constructed at the strong correlation spectral segments, and the models were tested and analyzed. The results showed that: (1) the characteristics of the near-infrared spectral absorption peaks around the wavelengths of 1 400 and 1 930 nm are significantly correlated with the rock water content; (2) the peak height, right half width and left half width at the wavelength of 1 400 nm have linear correlation with the water content obviously, and the peak height and right half width at the wavelength of 1 930 nm also have linear correlation with the water content obviously; (3) the multiple linear regression model can accurately express the correlation between the water content and the near-infrared spectrum, and the model can be used to predict the water content of water-bearing rock based on the characteristics of near-infrared spectrum. It provides basic modeling data for dynamic monitoring and evaluation of rock water content by using near infrared spectrum analysis technology.