基于POA-ELM的含煤地层异常构造分类

Classification of coal-bearing strata abnormal structure based on POA-ELM

为了更准确地对含煤地层异常构造识别分类,提出了一种基于鹈鹕优化算法(Pelican Optimization Algorithm, POA)和极限学习机(Extreme Learning Machine, ELM)的含煤地层异常构造识别分类模型POA-ELM。针对极限学习机随机生成输入权值和隐含层偏置导致性能不稳定的缺点,利用鹈鹕优化算法对极限学习机的输入权值和隐含层偏置进行寻优,从而改进极限学习机模型性能,并将POA-ELM应用到含煤地层异常构造的识别分类。首先利用COMSOL Multiphysics5.5建立小断层、冲刷带和陷落柱3种含煤地层仿真模型,以雷克子波作为震源信号,采用透射波法采集3种模型的槽波信号,建立槽波信号数据集。通过z-score法和主成分分析法(Principal Component Analysis, PCA)对槽波数据进行标准化和降维处理。通过MATLAB构建鹈鹕优化算法改进的极限学习机分类模型POA-ELM,对小断层、冲刷带和陷落柱进行分类,并通过准确率、精确率和召回率等评价指标以及交叉验证法对比和评估ELM、POA-ELM的分类性能,结果表明POA能够有效优化ELM,POA-ELM模型具有更高的分类准确率和更好的稳定性,POA-ELM对含煤地层异常构造的分类准确率可达99%以上。为验证POA-ELM的实际应用效果,将实际断层槽波数据进行小波去噪等预处理后,作为测试集导入POA-ELM模型进行识别,结果表明POA-ELM模型对实际断层识别准确率可达97%以上。基于同样的槽波数据集将POA-ELM与ELM、支持向量机(Support Vector Machine, SVM)和BP神经网络进行识别分类效果对比,结果表明POA-ELM模型的识别分类准确率最高。经研究与分析,POA能够有效优化ELM,POA-ELM模型能够准确分类地质构造,并有效识别出实际断层,效果优于其他方法。

In order to identify and classify the abnormal structures in coal-bearing strata more accurately,a POA-ELM model based on the pelican optimization algorithm(POA)and the extreme learning machine(ELM)is proposed.The performance of extreme learning machine is unstable because the input weights and hidden layer bias are generated randomly.The POA can be used to optimize the input weights and hidden layer bias of extreme learning machine,so as to improve the performance of extreme learning machine model.The POA-ELM model is applied to identify and classify the abnor-mal structures in coal-bearing strata.Firstly,three coal-bearing strata simulation models of small fault,scour zone and col-lapse column are established with the COMSOL Multiphysics5.5.The Ricker wave is the source signal.The in-seam wave signals are collected by wave transmission method,and the in-seam wave data set is established.Then the z-score method is used to standardize the in-seam wave data and the principal component analysis(PCA)is used to reduce the dimension.Secondly,the POA is used to optimize the extreme learning machine,and the POA-ELM classification model is construc-ted with MATLAB.The POA-ELM model is used to classify small fault,scour zone and collapse column.The classifica-tion performance of ELM and POA-ELM is evaluated and compared by cross-validation method and evaluation indices such as accuracy,precision and recall rate.The results show that the POA can effectively optimize the ELM,and the POA-ELM model has higher classification accuracy and better stability.The classification accuracy of POA-ELM for ab-normal structures can reach more than 99%.Thirdly,in order to verify the classification effect of POA-ELM in practical applications,after wavelet de-noising,z-score standardization and PCA dimensionality reduction,the real fault in-seam wave data are used as the test set and imported into the POA-ELM model for classification.The results show that the identification accuracy of POA-ELM model for real fault can reach more than 97%.Finally,based on the same data set,the classification effects of POA-ELM,ELM,support vector machine(SVM)and BP neural network are compared.The results show that the identification and classification accuracy of POA-ELM model is the highest.Through research and analysis,the POA can effectively optimize the ELM,and the POA-ELM model can accurately classify different geologic-al structures and effectively identify real faults,which is better than other methods.