沉积岩石的强度往往会受到水的影响, 含水量不同其影响程度也不相同。 因此, 沉积岩石的含水量测定对于后续评估其物理力学特性具有重要的价值。 在岩石含水量测定方面, 传统的方法往往费时、 费力, 而且破坏了工程结构的完整性。 近红外光谱分析技术是一种非常有潜力的方法, 具有实时、 无损等优点。 基于近红外光谱分析技术对砂岩的光谱特征以及含水量的反演进行了研究。 首先, 通过室内试验获取了砂岩试样不同饱和度的近红外光谱曲线; 然后, 对原始光谱曲线进行了异常曲线剔除以及一阶导数预处理, 消除噪声、 环境等因素的影响; 其次, 对R1(1 400 nm)和R2(1 900 nm)附近的两个吸收峰进行光谱特征变量提取以及归一化处理, 消除量纲和域值的影响; 接着, 基于最大信息系数对提取的光谱特征变量进行分析和筛选; 最后, 基于筛选后的光谱特征变量采用自行搭建的BP神经网分类器对砂岩的含水量进行了反演。 结果表明: (1)含水砂岩的近红外光谱吸收曲线在1 400和1 900 nm附近有着明显的吸收峰, 位于1 400 nm附近的吸收峰, 谱带比较宽缓, 位于1 900 nm附近的吸收峰, 谱带比较尖锐; 随着含水量的增加, 近红外光谱曲线在1 400和1 900 nm附近吸收峰的吸收强度也在增加, 具有明显的正相关性, 可作为砂岩含水量分析、 反演的主要谱段。 (2)根据计算的最大信息系数值, 1 400 nm附近的峰高与含水量的相关性最强, 同样1 900 nm附近的峰高与含水量的相关性最强; 1 400 nm附近的峰面积、 峰高和1 900 nm附近的峰高、 峰面积、 半高宽、 右肩宽, 共6个光谱特征变量, 其最大信息系数值>0.9, 可作为BP神经网络反演砂岩含水量的特征变量。 (3)利用最大信息系数筛选出1 400和1 900 nm附近两个吸收峰的特征变量进行BP神经网络建模, 所建立的砂岩含水量反演模型训练集准确率为90.3%, 测试集的准确率为83.9%, 说明基于近红外光谱分析技术砂石含水量的反演方法是可行的。

The strength of sedimentary rocks is often affected by water, and the influence degree is different with different water content. Therefore, the measurement of the water content of the rock has important value for the subsequent evaluation of its physical and mechanical properties. In measuring rock water content, traditional methods are often time-consuming and laborious, destroying the integrity of the structure. At present, near-infrared spectroscopy is a very potential method, with real-time and nondestructive advantages. In this paper, sandstone's spectral characteristics and the water content prediction are studied based on near infrared spectroscopy. Firstly, near-infrared spectrum curves of sandstone samples with different saturations were obtained through laboratory tests. Secondly, the first derivative of the original spectral curve is preprocessed to eliminate the influence of noise, environment, and other factors. Thirdly, the spectral characteristic variables of R1 (1 400 nm) and R2 (1 900 nm) were extracted and normalized to eliminate the influence of dimension and domain value. Fourthly, the extracted spectral characteristic variables are analyzed and screened based on the maximum information coefficient; Finally, the self-built BP neural network classifier is used to predict the water content of sandstone. The conclusions are as follows: (1) The near-infrared absorption curve of water-bearing sandstone has obvious absorption peaks near 1 400 and 1 900 nm, the absorption peak is near 1400 nm, the band is relatively broad, the absorption peak is near 1 900 nm, and the band is relatively sharp. As the water content increases, the absorption intensity of each absorption peak is also increasing, which has an obvious correlation and can be used as the main spectrum band for sandstone water content analysis and prediction. (2) According to the calculated maximum information coefficient value, the peak height near 1 400 nm has the strongest correlation with water content, and the peak height near 1900 nm has the strongest correlation with water content. Peak area and peak height near 1 400 nm, peak area, peak height, half-height width, and right shoulder width near 1 900 nm are 6 characteristic variables. The maximal information coefficient value is greater than 0.9, which can be used as characteristic variable for BP neural network to predict sandstone water content. (3) Using the maximum information coefficient to screen out the characteristic variables of the two absorption peaks at 1 400 and 1 900 nm for BP neural network modeling, the accuracy of the training set of the sandstone water content prediction model established by it was 90.3%, and the accuracy of the test set was 83.9%. The method based on near-infrared spectroscopy analysis technology to predict the water content of sand and gravel is feasible.