基于小波去噪的灰色Verhulst模型在变形监测中的应用  

Application of Grey Verhulst Model Based on Wavelet Denoising in Deformation Monitoring

作　　者：董坤烽 DONG Kunfeng(Yunnan Construction Investment First Survey and Design Co.,Ltd.,Kunming 650031,China)

机构地区：[1]云南建投第一勘察设计有限公司,云南昆明650031

出　　处：《地理空间信息》2022年第10期113-116,共4页Geospatial Information

基　　金：云南省教育厅科学研究基金资助项目(2021J0964)。

摘　　要：采用小波去噪与灰色Verhulst组合模型的研究,首先对于原始观测数据进行小波去噪处理,通过信噪比和均方差两个评价指标确定小波基函数、分解层数以及阈值函数;其次,将小波阈值去噪后处理的数据代入灰色Verhulst模型对沉降监测数据进行预测;最后,以某矿区西九采区回采工作面C观测线98号测点14期累计沉降观测数据为例,建立不同的预测模型进行对比。实验结果表明,基于小波去噪的灰色Verhulst组合模型的预测结果精度更高,能够更准确地表达其变形趋势。Aiming at the problem that the observation data may have the noise effect when establishing the prediction model of ground subsid-ence in the mining area,we adopted the combined model of wavelet denoising and gray Verhulst to study it.Firstly,we performed wavelet denois-ing processing on the original observation data,and determined the wavelet basis function,the number of decomposition layers and the threshold function through two evaluation indicators of signal-to-noise ratio and mean square error.And then,we substituted the processed data after wave-let threshold denoising into the gray Verhulst model to predict the settlement monitoring data.Finally,take the cumulative settlement observation data of No.98 measurement point of C observation line of working face in a certain mining area for example,we established different prediction models for comparison.Experimental result shows that the gray Verhulst combined model based on wavelet denoising has higher prediction re-sults and can express its deformation trend more accurately.

关 键 词：小波阈值去噪 灰色VERHULST模型 变形监测 

分 类 号：P258[天文地球—测绘科学与技术]