基于BP神经网络的煤层底板突水量等级预测  

Prediction of the level of water inrush from coal seam floorbased on BP neural network

作　　者：张承斌 ZHANG Chengbin(The Third Exploration Team of Shandong Coalfield Geologic Bureau,Taian 271000,China)

机构地区：[1]山东省煤田地质局第三勘探队,山东泰安271000

出　　处：《能源环境保护》2022年第6期101-109,共9页Energy Environmental Protection

摘　　要：为了减少水灾损失并提高煤矿底板突水量预测精度,基于肥城矿区相关矿井水文地质资料,选取水压、底板裂隙发育程度、断层落差、岩溶发育程度、含水层厚度、隔水层厚度等6项指标,建立了肥城矿区底板突水量等级预测模型,采用平均影响值(MIV)方法评价了神经网络模型中各自变量对突水量等级预测的影响程度。结果表明:该模型具有较高的预测精度;肥城矿区内的小型突水主要由含水层及隔水层的属性决定;大型及特大型突水的发生与断裂构造、岩溶和底板裂隙发育程度等因素密切相关。In order to reduce flood loss and improve the prediction accuracy of water inrush from coal seam floor,a prediction model of water inrush grade in Feicheng mining area was established based on the mine hydrogeological data of this area,selecting six indexes including water pressure,development degree of floor fissure,fault gap,karst development degree,aquifer thickness and aquiclude thickness.The method of mean impact value(MIV)was used to evaluate the influence of each variable in the neural network model on the prediction of water inrush grade.The results show that this model has high prediction accuracy.Small water inrush in Feicheng mining area is mainly determined by the attributes of the aquifer and aquiclude.The occurrence of large and extra-large water inrush is closely related to the factors such fault structure,karst and development degree of floor fissure.

关 键 词：底板突水 BP神经网络 预测模型 平均影响值(MIV) 肥城矿区 

分 类 号：X45[环境科学与工程—灾害防治] X523