灰色Verhulst模型在矿区沉降预测中应用探讨

Discussion on Grey Verhulst Model Used in Mining Subsidence Prediction

在沉陷日常监测中,受各种不利因素影响,数据不易获得,数据量往往不多,从而影响了沉陷的预计精度。针对这个问题,在对数据等时间距化的基础上,运用了GM(1,1)和灰色Verhulst模型对沉降量进行了预计。结合矿区地表实测沉降进行了实例验证,结果表明灰色Verhulst模型对矿区沉降量的预计比GM(1,1)更为有效。

In subsidence routine surveillance, influenced by various unfavorable factors, the data is not easy to get and amount of data is often not much, which affect the subsidence prediction accuracy.Aiming at this problem, based on data such as time distance, the GM(1,1) and grey Verhulst model are used to predict the settlement. Combined with a surface measured subsidence example verification, the results show that grey Verhulst model of mining subsidence is expected to more effective than GM(1,1).