3D Visualization Monitoring and Early Warning System of a Tailings Dam-Gold Copper Mine Tailings Dam in Zijinshan, Fujian, China

被引:13
作者
Nie, Wen [1 ,2 ,3 ]
Luo, Minghua [1 ,2 ]
Wang, Yunmin [2 ,3 ]
Li, Ruixiang [4 ]
机构
[1] Southwest Petr Univ, Sch Geosci & Technol, Chengdu, Peoples R China
[2] State Key Lab Safety & Hlth Met Mines, Maanshan, Peoples R China
[3] Sinosteel Maanshan Gen Inst Min Res Co LTD, Maanshan, Peoples R China
[4] Zijin Min Co Ltd, Shanghang, Peoples R China
关键词
tailings dam; 3D geographic information system; cloud-side collaborative technique; monitoring and early warning system; 3D visualization; PREDICTION; MODEL;
D O I
10.3389/feart.2022.800924
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
A 3D tailings dam visualization early warning system was developed based on GIS (geographic information system) combining ARIMA (autoregressive integrated moving average model) and 3S (RS, GIS, GPS) technology for prediction of phreatic line changes and tailing dam deformation. It was applied for monitoring and early warning for the gold-copper tailing dam in Zijinshan Dadongbei tailing pond. The system consists of equipment management, data management, prediction, monitoring and early warning, and 3D visualization modules. It is able to do data management, visualization and disaster prediction, and early warning based on 79 monitoring points of rainfall, infiltration line, and deformation of the tailing dam in the Zijinshan mine. The design and application of the system reflect its features of rich functionality, high practicality, intuitive effect, and high reference value. The system solves the problems of low visualization of monitoring data, poor management of multiple data, and feasible prediction and early warning of point-surface combination. It realizes high-precision prediction of key factors and real-time warning of disaster.
引用
收藏
页数:14
相关论文
共 56 条
[1]   Landslide data analysis using various time-series forecasting models [J].
Aggarwal, Akarsh ;
Alshehri, Mohammed ;
Kumar, Manoj ;
Alfarraj, Osama ;
Sharma, Purushottam ;
Pardasani, Kamal Raj .
COMPUTERS & ELECTRICAL ENGINEERING, 2020, 88
[2]  
Al Faisal A., 2018, J REMOTE SENS GIS, V7, P1, DOI [10.4172/2469-4134.1000236, DOI 10.4172/2469-4134.1000236]
[3]   Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models [J].
Al-Zahrani, Muhammad A. ;
Abo-Monasar, Amin .
WATER RESOURCES MANAGEMENT, 2015, 29 (10) :3651-3662
[4]   Positional accuracy and convergence time assessment of GPS precise point positioning in static mode [J].
Ayhan M.E. ;
Almuslmani B. .
Arabian Journal of Geosciences, 2021, 14 (13)
[5]  
Bai Y., 2020, CHINA METAL B, P65
[6]  
Cahyono A. B., 2021, IOP Conference Series: Earth and Environmental Science, V731, DOI 10.1088/1755-1315/731/1/012023
[7]   Zigbee-based prediction system for coal rock dynamic disasters [J].
Chen Wenxue ;
Wang Longkang ;
Wang Hui ;
Zhu Zhuwu .
ISMSSE 2011, 2011, 26
[8]   Catalogue of example instrumentation and monitoring systems for tailings dams in Australia [J].
Clarkson, Luke ;
Williams, David .
MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY, 2021, 130 (02) :119-129
[9]   An Overview of Conventional Tailings Dam Geotechnical Failure Mechanisms [J].
Clarkson, Luke ;
Williams, David .
MINING METALLURGY & EXPLORATION, 2021, 38 (03) :1305-1328
[10]   Real-time monitoring of tailings dams [J].
Clarkson, Luke ;
Williams, David ;
Seppala, Jaakko .
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS, 2021, 15 (02) :113-127