Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush
Xin Wang, Zhimin Xu, Yajun Sun, Jieming Zheng, Chenghang Zhang, Zhongwen Duan
Abstract
As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning, the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years. Due to the many factors affecting water inrush and the complicated water inrush mechanism, many factors close to water inrush may have precursory abnormal changes. At present, the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level, water influx, and temperature, and performs water inrush early warning through the abnormal change of a single factor. However, there are relatively few multi-factor comprehensive early warning identification models. Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases, 11 measurable and effective indicators including groundwater flow field, hydrochemical field and temperature field are proposed. Finally, taking Hengyuan coal mine as an example, 6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model, a multi-factor linear recognition model, and a comprehensive intelligent early-warning recognition model. The results show that the correct rate of early warning can reach 95.2%.