BP神经网络判别模型在中关铁矿突水水源预测中的应用
Application of BP neural network discriminant model in prediction of water inrush source in Zhongguan iron mine
王俊鹏 1杨智淇 2庞旭静1
作者信息
- 1. 华北理工大学矿业工程学院,河北唐山 063200
- 2. 山西省地勘局213地质队有限公司,山西临汾 041000
- 折叠
摘要
中关铁矿属国内典型大水矿床,其地下水系统相对复杂,为保障工作人员的生命财产安全,需及时分析、准确预测矿井生产过程中突水水源.依据在中关铁矿三个开采水平面现有出水点采集水样的水质测试结果,分析中关铁矿地区水质特征,以 BP神经网络判别模型建立矿井水源判别模型,根据测试结果数据分析,BP神经网络判别模型对于奥陶系灰岩水与闪长岩裂隙水判别正确率为 100%、对于混水判别的正确率为 80%.BP神经网络判别模型在中关铁矿突水水源预测中具有较高的准确性,为防治水提供理论依据.
Abstract
Zhongguan Iron Mine is a typical large water deposit in China,and its groundwater system is relatively com-plex.In order to ensure the safety of life and property of workers,it is necessary to analyze and accurately predict the source of water inrush in the process of mine production in time.Based on the water quality test results of water samples collected from the existing water outlet points at three mining levels in Zhongguan Iron Mine,the water quality characteristics of Zhongguan I-ron Mine area are analyzed,and the BP neural network discriminant model is used to establish the mine water source discrimi-nant model.According to the data analysis of the test results,the BP neural network discriminant model has a correct rate of 100%for Ordovician limestone water and diorite fissure water,and a correct rate of 80%for mixed water.The BP neural net-work discriminant model has high accuracy in the prediction of water inrush source in Zhongguan Iron Mine,which provides a theoretical basis for water prevention and control.
关键词
BP神经网络判别模型/地下水/突水水源判别/中关铁矿Key words
BP neural network discriminant model/groundwater/water inrush source discrimination/Zhongguan iron mine引用本文复制引用
基金项目
河北省自然科学基金(D2017209229)
出版年
2024
NETL
NSTL
万方数据