Abstract: Aiming at the problem of low prediction accuracy in tectonic coal thickness prediction, a new method of Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) algorithm was proposed for predicting tectonic coal thickness. Firstly, Principal Component Analysis (PCA) was used to reduce the dimensionality of 3D seismic attributes, which reduced the dimension of seismic attributes, and eliminated the correlation among variables. Then, a Hybrid Kernel Extreme Learning Machine (HKELM) model with global polynomial kernel function and local Gaussian radial basis kernel function was constructed, and the kernel parameters of HKELM were optimized by using PSO algorithm. Furthermore, in order to solve the problem of easily falling into the local optimum for the PSO algorithm, the idea of simulated annealing, the inertia weight decreasing with the number of iterations, and the mutation operation based on reverse learning were added to the PSO algorithm, which made it easier jump out of local minimum points and get better results. In addition, in order to enhance the generalization ability of model, L2 regularization term was added based on the kernel function, which could effectively avoid the influence of noisy data and abnormal points on the generalization performance of model. Finally, the improved prediction model was applied to 15# coal seam in the central part of Luonan No.2 mining area in Xinjing Mining Area of Yangquan Coal Mine, and the predicted thickness of tectonic coal in the mining area guaranteed high consistency with the actual geological data. The experimental results show that the prediction error of the prediction model of tectonic coal thickness constructed by using the improved PSO algorithm to optimize HKELM is smaller, therefore the proposed method can be extended to the prediction of tectonic coal thickness in the actual mining area.

Key words: Principal Component Analysis(PCA), Particle Swarm Optimization (PSO), kernel function, Extreme Learning Machine (ELM), tectonic coal, thickness prediction

摘要： 在构造煤厚度的预测中，针对预测精度不高的问题，提出利用粒子群优化（PSO）算法优化极限学习机（ELM）的方法来对构造煤厚度进行预测。首先，利用主成分分析（PCA）对三维地震属性进行降维处理，在降低地震属性的维数的同时消除变量之间的相关性。然后，构建全局多项式核函数和局部高斯径向基核函数混合核极限学习机（HKELM）模型，并利用PSO算法优化HKELM的核参数。同时，针对PSO算法存在容易陷入局部最优的问题，在PSO算法中加入模拟退火的思想和随迭代次数减小的惯性权重，以及基于反向学习的变异操作，使PSO算法可以更容易跳出局部极小值点，得到更优结果。此外，为了增强模型的泛化能力，在核函数的基础上加入L2正则项，有效地避免了噪声和异常点对模型泛化性能的影响。最后，将预测模型应用到阳煤集团新景矿区芦南二采区中部15#煤层中，预测得到的采区构造煤厚度与实际地质资料具有较高的一致性。实验结果表明，利用改进PSO算法优化HKELM构建构造煤厚度预测模型的预测误差较小，可以推广用于实际采区的构造煤厚度预测。

关键词: 主成分分析, 粒子群优化, 核函数, 极限学习机, 构造煤, 厚度预测