CatBoost mine pressure appearance prediction based on Bayesian algorithm optimization
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摘要: 通过传统的监测手段获取矿压数据并采用统计学或机器学习算法对矿压进行预测已不能满足矿山智能化发展要求，需要寻求新的方法提升矿压数据监测及矿压预测的准确性和实时性。基于三维相似物理模型试验，搭建分布式光纤监测系统，沿模型走向和高度2个方向预埋分布式光纤，在模拟工作面开采过程中采集来压数据，并引入光纤布里渊频移平均变化度作为判断是否来压的指标；通过对光纤监测数据进行噪声去除、归一化及相空间重构等预处理，将一维初始监测数据转换为三维数据；使用贝叶斯算法对CatBoost算法进行迭代参数寻优，在达到最大迭代次数后将最优参数组合装载到CatBoost算法中，通过训练得到矿压显现预测模型。结果表明：贝叶斯算法比传统网格搜索法的迭代次数更少、误差更小；与随机森林（RF）、梯度提升决策树（GBDT）和极值梯度提升树（XGBoost）算法相比，CatBoost算法的预测精度更高、泛化能力更强；基于贝叶斯算法优化的CatBoost矿压显现预测模型能准确预测出测试集中的3次来压，且整体预测趋势与实测值较为吻合，平均绝对误差为0.009 1，均方根误差为0.007 7，决定系数为0.933 9。Abstract: Obtaining mine pressure data through traditional monitoring methods and using statistical or machine learning algorithms to predict mine pressure can no longer meet the requirements of intelligent development in mines. It is necessary to seek new methods to improve the accuracy and real-time performance of mine pressure data monitoring and prediction. Based on three-dimensional similar physical model experiments, a distributed fiber optic monitoring system is constructed. The distributed fiber optic cables are pre-embedded along the model's direction and height. Pressure data is collected during the simulated mining process of the working face, and the optical fiber Brillouin frequency shift mean variation degree is introduced as an indicator to determine whether the pressure is coming. By preprocessing the optical fiber monitoring data such as noise removal, normalization and phase space reconstruction, the one-dimensional initial monitoring data is converted into three-dimensional data. The method uses Bayesian algorithm to iteratively optimize the parameters of the CatBoost algorithm. After reaching the maximum number of iterations, the optimal parameter combination is loaded into the CatBoost algorithm. The prediction model for mine pressure appearance is obtained by training. The results show that the Bayesian algorithm has fewer iterations and smaller errors than traditional grid search methods. Compared with random forest (RF), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost), the CatBoost algorithm has higher prediction accuracy and stronger generalization capability. The CatBoost mine pressure appearance prediction model optimized by the Bayesian algorithm can accurately predict the three weighting in the test set. The overall prediction trend is in line with the measured value, with mean absolute error of 0.0091, root-mean-square error of 0.0077, and determination coefficient of 0.933 9.
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图 6 工作面开采全过程矿压曲线
Figure 6. Mine pressure curve of the whole process of working face mining
图 8 光纤布里渊频移平均变化度实测值与预测值
Figure 8. Measured values and predicted values of optical fiber Brillouin frequency shift mean variation degree
图 9 不同算法泛化能力对比
Figure 9. Comparison of generalization ability of the different algorithms
表 1 三维相似物理模型基本参数
Table 1. Basic parameters of the 3D physical similarity model
参数 值 参数 值 长度/mm 3600 宽度/mm 2000 高度/mm 2000 开挖步数 54 煤层厚度/mm 100 开挖步距/mm 50 几何相似比 1∶200 开挖时间间隔/h 0.5 容重相似比 1.56∶1 应力相似比 380∶1 
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表 2 光纤布里渊频移平均变化度相空间重构
Table 2. Phase space reconstruction of optical fiber Brillouin frequency shift mean variation degree
序号 开挖
距离
/mm$ {x_1} $ $ {x_2} $ $ {x_3} $ $ Y $ 序号 开挖
距离/mm$ {x_1} $ $ {x_2} $ $ {x_3} $ $ Y $ 1 200 0 0.000026 0.001579 0.002474 27 1500 0.864075 0.838863 0.721304 1.000 000 2 250 0.000026 0.001579 0.002474 0.002153 28 1550 0.838863 0.721304 1.000 000 0.869531 3 300 0.001579 0.002474 0.002153 0.001354 29 1600 0.721304 1.000 000 0.869531 0.413876 4 350 0.002474 0.002153 0.001354 0.002186 30 1650 1.000 000 0.869531 0.413876 0.574574 5 400 0.002153 0.001354 0.002186 0.019577 31 1700 0.869531 0.413876 0.574574 0.336719 6 450 0.001354 0.002186 0.019577 0.020404 32 1750 0.413876 0.574574 0.336719 0.422573 7 500 0.002186 0.019577 0.020404 0.015886 33 1800 0.574574 0.336719 0.422573 0.656925 8 550 0.019577 0.020404 0.015886 0.014098 34 1850 0.336719 0.422573 0.656925 0.544454 9 600 0.020404 0.015886 0.014098 0.028925 35 1900 0.422573 0.656925 0.544454 0.547804 10 650 0.015886 0.014098 0.028925 0.046407 36 1950 0.656925 0.544454 0.547804 0.572263 11 700 0.014098 0.028925 0.046407 0.027881 37 2000 0.544454 0.547804 0.572263 0.731946 12 750 0.028925 0.046407 0.027881 0.016708 38 2050 0.547804 0.572263 0.731946 0.453583 13 800 0.046407 0.027881 0.016708 0.452190 39 2100 0.572263 0.731946 0.453583 0.564985 14 850 0.027881 0.016708 0.452190 0.491797 40 2150 0.731946 0.453583 0.564985 0.815280 15 900 0.016708 0.452190 0.491797 0.689201 41 2200 0.453583 0.564985 0.815280 0.751002 16 950 0.452190 0.491797 0.689201 0.570864 42 2250 0.564985 0.815280 0.751002 0.745354 17 1000 0.491797 0.689201 0.570864 0.599183 43 2300 0.815280 0.751002 0.745354 0.692883 18 1050 0.689201 0.570864 0.599183 0.654041 44 2350 0.751002 0.745354 0.692883 0.796233 19 1100 0.570864 0.599183 0.654041 0.447253 45 2400 0.745354 0.692883 0.796233 0.940375 20 1150 0.599183 0.654041 0.447253 0.721625 46 2450 0.692883 0.796233 0.940375 0.840692 21 1200 0.654041 0.447253 0.721625 0.698059 47 2500 0.796233 0.940375 0.840692 0.722012 22 1250 0.447253 0.721625 0.698059 0.772544 48 2550 0.940375 0.840692 0.722012 0.673414 23 1300 0.721625 0.698059 0.772544 0.640361 49 2600 0.840692 0.722012 0.673414 0.593709 24 1350 0.698059 0.772544 0.640361 0.864075 50 2650 0.722012 0.673414 0.593709 0.473591 25 1400 0.772544 0.640361 0.864075 0.838863 51 2700 0.673414 0.593709 0.473591 0.394052 26 1450 0.640361 0.864075 0.838863 0.721304
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表 3 CatBoost算法参数
Table 3. Parameters of CatBoost algorithm
参数 名称 作用 默认值 搜索范围 iterations 最大树数 提升精度 1 000 [40，130] learning_rate 学习率 提升精度 0.03 [0.01，0.30] depth 树的最大深度 提升精度 6 [3，10] l2_leaf_reg L2正则化 正则化，减小过拟合 3 [1，10]
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表 4 参数优化结果对比
Table 4. Comparison of the parameter optimization results
优化方法 迭代次数 MAE RMSE 网格搜索法 55 0.073 0.089 贝叶斯算法 30 0.065 0.079
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表 5 不同算法性能指标对比
Table 5. Comparison of performance indicators of different algorithms
算法 RMSE MAE R2 RF 0.017 9 0.013 5 0.874 6 GBDT 0.032 5 0.037 1 0.859 9 XGBoost 0.011 5 0.009 2 0.907 8 CatBoost 0.009 1 0.007 7 0.933 9
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