基于灰色关联分析-Elman神经网络的矿井小断层延展长度预测

Prediction Model of Small Faults Extending Length Based on Grey Correlation Analysis and Elman Neural Network

摘要: 阐述了矿井小断层走向延展长度预测Elman神经网络模型的构建、训练及模拟方法，结合实测样本数据，利用灰色关联分析法分析了小断层走向延展长度与断层落差、走向、倾角及倾向等影响因素的相关性，确定了小断层走向延展长度的预测参数，并运用matlab软件建立了基于Elman神经网络的矿井小断层走向延展长度预测模型。实际应用表明，该模型的预测精度较高，比较符合实际情况。

Abstract: The article elaborates the building, training and simulation method of Elman neural network for predicting the horizontal extending length of small faults in coal seam. Based on grey correlation analysis of the influence factors, we select four predictive parameters including the fault throw, fault strike, fault dip and dip direction. Combined with the typical sample data, we use Matlab software to construct a new network prediction model of small faults extending length prediction, and practical application is given to illuminate the feasibility of the modules.