基于未确知聚类法的底板采动破坏深度动态预测  

Dynamic forecasting of mining-induced failure depth of floor based on unascertained clustering method

作　　者：程爱平[1] 高永涛[1] 梁兴旺 季毛伟[1] 王存文[1] 高艳华[1] 

机构地区：[1]北京科技大学土木与环境工程学院,北京100083 [2]中煤平朔集团有限公司井工一矿,山西朔州036006

出　　处：《采矿与安全工程学报》2014年第5期739-744,共6页Journal of Mining & Safety Engineering

基　　金：国家自然科学基金项目(51174016)

摘　　要：根据微震监测结果,利用未确知聚类优化法,选取采深、煤层倾角、采厚、构造影响程度4个主要影响因素作为判别指标,建立煤矿底板采动破坏深度动态预测模型。利用微震实测的18组数据作为训练样本,以样本均值为聚类中心,采用信息熵理论确定各判别指标的权重,通过计算样本的多指标综合测度与所属类别样本均值乘积之和获得底板采动破坏深度的预测值,并对样本数据进行逐一检验。为进一步验证该方法的可靠性,另选5组样本进行预测,将预测值与微震实测结果做了比较。研究结果表明:底板采动破坏深度的预测值与实测值的平均相对误差不超过1%,底板采动破坏深度动态预测模型是可靠实用的,可以在同类矿山进行推广应用。According to the microseismic monitoring results, four main influencing factors, that is, mining depth, coal seam dip angle, mining thick and structure impact level, were regarded as judgment indexes and used to establish the dynamic forecasting model of mining-induced failure depth of floor by unascertained clustering method. The mean value of training samples, which come from 18 datasets measured by microseismic monitoring, were set as cluster center, and the weight indexes of judgment were determined by information entropy theory. Through calculating the product sum of multi-index comprehensive measurement of sample and the corresponding sample average, the forecasting value of the mining-induced failure depth of floor was obtained, and then the model was identified by the whole samples. In addition, to further test its reliability, the method was applied to forecast the other five sam-ples to compare the forecasted values with the measured values. The results show that the average of relative error between forecasted values and measured values is less than 1%. The dynamic forecasting model of mining-induced failure depth of floor is reliable and practical, and it can be popularized and applied to the similar mines.

关 键 词：微震监测 未确知聚类 底板采动破坏深度 预测 

分 类 号：TD745[矿业工程—矿井通风与安全]