Water-inrush Assessment Using a GIS-based Bayesian Network for the 12-2 Coal Seam of the Kailuan Donghuantuo Coal Mine in China

Abstract

The Donghuantuo coal mine is geologically unusual, with 60 normal faults, 18 reverse faults, and 1 syncline. The coal seam floor is highly fractured and the fractures act as conduits for groundwater, which flows from the Ordina limestone aquifer into the no. 12 coal seam. From 2005 to 2010, there were 7 water-inrush disasters through the floor of this coal seam. The largest waterinrush event exceeded 63 m 3 /min; there are five points where the water-inrush continues to exceed 1.0 m 3 /min. Comprehensive modeling of the probability of water-inrush through the floor is required to reduce the likelihood and severity of such events. The water-inrush situation was assessed using a GIS-based Bayesian network (BN). In the developed BN-GIS model, the geometry of the coal mine working face was incorporated in suitable detail and resolution. The results of the modeling compared well with field water-inrush observations. Based on documented water-inrush events, the accuracy of the fit of the model data is 83.4 %, and the probability of making an incorrect prediction is less than 0.5, which means that using this method could significantly enhance coal production at the mine.

The Effects of Caving of a Coal Mine’s Immediate Roof on Floor Strata Failure and Water Inrush

“…Hence, the depth at which coal deposits are extracted has increased considerably (Please et al 2013;Singh and Singh 2009). Deep coal mining has inherent risks associated with it due to in situ stress and hydraulic damage conditions produced by the overburden pressure, tectonic movements, and pressurized water in aquifers underlying the floor strata (Dong et al 2012;Li 1999;Peng 2008). Longwall mining is commonly used for underground coal extraction (Abbas et al 2012;Singh 2014).…”

Numerical Simulation on the Seepage Properties of Soil‐Rock Mixture

“…Many scholars devote themselves to the research of water inrush, and more and more methods are used to evaluate the risk of water inrush in tunnels [5][6][7][8][9][10][11][12]. e causes of water inrush in the tunnel are complicated, being not only affected by the geological conditions but also influenced by construction disturbances [13][14][15][16].…”

Comparing control methods of water inrush disaster using mathematical programming: modelling, analysis and a case study

“…Likewise, Geographic Information System (GIS) techniques were introduced to construct dynamic three-dimensional model for water flow spreading process in order to develop emergency plans (Li et al 2013). Dong et al (2012) adopted a GIS-based Bayesian network to assess water inrush scenarios. Then, Shi et al (2014) made a water inrush evaluation of coal seam floor by integrating the water inrush coefficient and the information of water abundance.…”