Research On Rock Permeability Characteristics And Prediction Of Water Inflow In Maluping Mine

Abstract:Underground water gushing has becoming an important factor which has a big influence on the safety of deep mining. A series of problems such as ore sliming, poor security, high loss rate of dilution and transportation difficulties are caused by the gushing water, as a result, the goal to mine safety, efficiently and economicaly is haldly to achieve. In order to ensure the normal operation and production in mines, underground water control is particularly important and the first necessary job to do is predicting the groundwater discharge accurately. To solve these problems existing in Maluping mine, this paper carries out corresponding study on ore-bearing rock permeability characteristics and mine water inflow forecast. The main research contents are as follows:(1) The hydrogeological data of the mining area is widely collected and the water filling passage and the factors which have an affact on water filling are figured out on the basis of analysis of groundwater dynamic characteristic and hydraulic connection.(2) Two typical kinds of rock, dolomite and phosphate, are taken back into laboratory and processed into standard samples. To figure out law of rock permeability characteristics under load of differert confining pressure and axial pressure, an experiment is carried out and the approximate calculation formula is obtained by linear fitting method.(3) An engineering geological survey about the joint fissure on the hanging hall is conducted firstly. Then the permeability coefficient of corresponding rock mass is calculated using the Mathematic software and the rough distribution range of rock mass permeability coefficient is worked out.(4) A LS-SVM predict model of mine water inflow is established based on the theory of support vector machine and the principle of the least square method. The data of the mine water inflow from2007to2012is used to train the model and the mine water inflow from January to June2013is predicted after the model is well trained. The results show that the LS-SVM prediction model is more accurate than Dynamic Neural Network so it can meet the demand of practical engineering well.(5) Several water control measures mianly including the surface water control measures, underground mine drainage safety technical measures and the measures for prevention and control of water are proposed to meet the requirements of safety mining at last.