为了克服单一模型预测精度很难进一步提高的不足, 利用近红外光谱分析结合基于Stacking框架的异构集成学习模型实现对油页岩含油率的检测。 以松辽盆地某区块所取230个油页岩岩芯样本为研究对象, 使用低温干馏法测量油页岩样本的含油率, 同时扫描每个样本对应的近红外光谱数据。 样本使用蒙特卡洛算法进行异常样本剔除, 将剔除异常样本后的213个数据按照3∶1的比例随机划分为训练集和预测集。 利用去趋势加基线校正方法进行预处理消除光谱数据中噪声和基线漂移, 利用随机森林算法进行波长重要性排序并保留重要波长, 在此基础上采用CARS算法进行特征波长提取, 进一步降低数据维度。 最后, 构建以PLS, SVM, RF和GBDT为初级学习器, PLS回归模型为次级学习器的Stacking集成学习模型, 各初级学习器模型参数使用网格搜索进行寻优。 使用决定系数和预测均方根误差作为各模型的评价指标, 探究单一模型和集成学习模型对油页岩含油率预测的准确性。 研究结果表明, RF-CARS方法能够有效筛选重要波长, 进而提高模型效率。 基于Stacking的异构集成学习模型与单一模型(SVM和PLS)和同构集成学习模型(RF和GBDT)相比有更好的预测效果和更强的稳定性。 在多次随机划分数据集的基础上, Stacking集成学习模型的平均决定系数R2为0.894 2, 相比于其他单一模型平均提高了0.062 3； RMSEP为0.586 9, 比其他模型平均降低了0.147 4。 说明, 基于Stacking的异构集成学习模型能够组合初级学习器的优势, 提高油页岩含油率预测精度, 为油页岩含油率快速检测提供了一种新方法。

Aims to overcome the shortcomings that the prediction accuracy of a single model is hard to improve further, A heterogeneous ensemble learning model based on the Stacking framework, combined with near-infrared spectroscopy analysis technology, was adopted to detect the oil content in oil shale in this study. A total of 230 oil shale core samples, collected from some block in Songliao Basin, were taken as the research object, whose oil content was measured by the low-temperature dry distillation method, and near-infrared spectral data corresponding to each sample was scanned simultaneously. The Monte Carlo algorithm was employed to eliminate outlier samples, and 213 samples, after removing outliers, were randomly divided into a training set and test set according to the ratio of 3∶1. The detrend coupled with the baseline correction method was used to eliminate the influence of noise and baseline drift in spectral data. After that, the random forest algorithm (RF) was used to extract the characteristic wavelength according to the importance of wavelength. In order to further reduce the data dimension, the CARS algorithm was used to extract the characteristic wavelength. Finally, PLS, SVM, RF and GBDT, whose parameters were optimized by grid search, were adopted as primary learners, and the PLS regression modelwas adopted as secondary learners to build the stacking ensemble learning model. The accuracy of the single and ensemble learning models for oil shale oil content prediction was compared under evaluation indicators of R2 and RMSE. The research results show that the RF-CASR method can effectively screen important wavelengths and improve the efficiency of the model, thereby improving the model efficiency. Heterogeneous integrated learning models based on Stacking have better predictive performance and greater stability than single models (SVM, PLS) and homogeneous integrated learning models (RF, GBDT). Based on multiple random divisions of the data set, the average R2 of the Stacking ensemble learning model is 0.894 2, an average increase of 0.062 3 compared with other models; the RMSEP of 0.5869 is an average of 0.147 4 lower than other models. The results of this study show that the heterogeneous integrated learning model based on stacking can combine the advantages of primary learners to predict the oil content of oil shale quickly and accurately, which provides a new fast and portable method for oil shale oil content detection.