煤炭工程 ›› 2020, Vol. 52 ›› Issue (9): 106-110.doi: 10.11799/ce202009021

• 研究探讨 • 上一篇    下一篇

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

张宝   

  1. 山西潞安矿业集团有限责任公司
  • 收稿日期:2019-10-13 修回日期:2020-03-30 出版日期:2020-09-15 发布日期:2020-11-24
  • 通讯作者: 张宝 E-mail:546099038@qq.com

Prediction technology of gas emission in small fault structure area based on BP neural network

  • Received:2019-10-13 Revised:2020-03-30 Online:2020-09-15 Published:2020-11-24

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。

关键词: 小断层, 瓦斯涌出, BP神经网络, 构造区域

Abstract: According to the problems existing in the prediction of gas emission in small fault structure area, this paper determines the gas parameters before and after the mining in the small fault tectonic area of Wu-yang mine, Chang-cun mine, Li-cun mine and Yu-wu mine in Lu'an mining area, then, the law of gas emission in the small fault tectonic area is analyzed and the main factor index system of the small fault tectonic area which affects the gas emission is established. Using the BP neural network analysis method, the relationship between geological structure of small fault tectonic and gas emission is studied, furthermore, a prediction model for gas emission in small fault structure area is established based on BP neural network. The application in Lu'an mining area shows that, the error between the method and the measured data is less than 5%, which can effectively predict the gas emission at different locations in the small fault structure area.

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