基于BP神经网络的小断层构造区域瓦斯涌出预测方法研究  

Gas emission prediction in small fault structure region based on BP neural network

作　　者：张宝 何健 ZHANG Bao;HE Jian(Shanxi Lu'an Mining Group Co.,Ltd.,Changzhi 046204,China)

机构地区：[1]山西潞安矿业集团有限责任公司,山西长治046204

出　　处：《煤炭工程》2020年第9期106-110,共5页Coal Engineering

摘　　要：为了准确预测小断层构造区域的瓦斯涌出情况,通过测定小断层构造区域的瓦斯参数,探讨小断层构造对瓦斯涌出的影响作用,分析小断层构造区域的瓦斯涌出规律,建立小断层构造区域瓦斯涌出影响因素指标体系和基于BP神经网络的小断层构造区域瓦斯涌出预测模型,并在潞安矿区进行应用。结果表明:断层前后100m范围内瓦斯涌出呈现“增高-减小-增高”的U型变化规律,当断层落差大于3m后对瓦斯涌出的影响作用显著增大,逆断层处的瓦斯涌出量比正断层处相对升高更加明显;小断层区域瓦斯涌出预测模型的预测结果与实测数据误差小于5%,可以有效的预测小断层构造区域不同位置的瓦斯涌出量。In order to accurately predict the gas emission in the small fault structure region,the gas parameters are measured,and the influence of small fault structure on gas emission and the gas emission law is analyzed.The influencing factor index system of gas emission in small fault structure is established.Based on BP neural network,the prediction model of gas emission in small fault tectonic area is established and applied in Luan mining area.The results show that the gas emission in 100m in the front and back of the fault varies in a U-type curve.When the fault drop is greater than 3m,the effect of small fault structure on gas emission increases significantly,and the gas emission at the thrust fault rises more significantly than at the normal fault.The error between of the proposed method is less than 5%,which is valid in gas emission predicting at different locations in the small fault structure area.

关 键 词：小断层 瓦斯涌出 BP神经网络 构造区域 

分 类 号：TD712[矿业工程—矿井通风与安全]