摘要
针对Knothe时间函数模型对开采沉陷过程的预计结果与沉陷实际发生过程不完全符合的问题,分析了该模型的缺陷,提出了改进的Knothe时间函数开采沉陷预计模型。结合河北省武安市红旗铁矿6300综放工作面地表沉陷实测数据,构建了改进的Knothe时间函数开采沉陷预计模型,并进行了模型精度分析。研究表明:经过累计387 d观测,改进的Knothe时间函数开采沉陷预计模型的预计值与实测值的误差为0.2~73.6 mm,平均误差为35.2 mm,优于BP神经网络模型(误差为8.1~143 mm,平均为49.9 mm)、SVM模型(误差为0.7~105.1 mm,平均为35.8 mm)以及概率积分法模型(误差为18.2~180.5 mm,平均为102.6 mm),对于高精度预计该矿开采沉陷具有一定的作用。
The prediction results of mining subsidence development of Knothe time function model is not consistent to the actual situation. In order to solve the problem and improve the prediction accuracy of mining subsidence based on Knothe time function model, the defects of the model is discussed in detail, and the improved Knothe time function mining subsidence prediction model is proposed. Based on the actual mining subsidence monitoring data of 6300 fully mechanized working face of Hongqi Iron Mine in Wu＇an County ,Hebei Province ,the improved Knothe time function mining subsidence prediction model is established, and its prediction accuracy is analyzed. The study results show that during the monitoring process of mining subsidence for 387 d, the error between prediction values of the improved model and actual measured values is 0. 2 - 73.6 ram, the average error is 35.2 mm ,which is superior to the ones of BP neural network model （ the error between predictions values and actual measured values is 8. 1 - 143 mm, the average error is 49. 9 mm） ,SVM model （the error between prediction values and actual measured values is 0. 7 - 105. 1 mm,the average error is 35.8 mm） and probability integral method model （the error between prediction values and actual measured values is 18.2 - 180. 5 mm, the average error is 102. 6 mm） , which further indicated that the improved model proposed in this paper is help for improving the mining subsidence prediction accuracy of the mine.
作者
李建
Li Jian(School of Geological Surveying and Mapping Engineering, Chongqing Vocational Institute of Engineering, Chongqing 402260, Chin)
出处
《金属矿山》
CAS
北大核心
2018年第3期132-136,共5页
Metal Mine
