摘要
针对变形监测数据中粗差探测与修复问题,提出一种基于局部均值分解(LMD)的粗差探测方法,并结合三次样条插值方法对粗差点进行修复.通过LMD方法对变形序列进行分解得到其PF分量,根据高频分量的奇异点确定可疑粗差点,将分解分量去除高频分量进行重构,利用数学检验方法确定粗差点位置.剔除粗差点后,采用三次样条插值方法进行修复粗差点.研究结果表明:局部均值分解方法在变形监测数据处理中的粗差探测效果明显,三次样条插值修复也基本准确,为大坝变形多尺度分析奠定了较好的基础.
For the problem of gross error detection and correction, this paper deeply analyzed the theory of Local Mean Decomposition（LMD） and the spline method. Then the LMD method of gross error detection is proposed. By using the LMD method, the deformation sequence is decomposed. Through the theory of high frequency Product Function and mathematical statistics, the gross error is found. Experimental results of two models showed that the LMD method can detect gross error effectively, so it is able to overcome the problem of gross error detection and correction. So the LMD method is very suitable to be applied to detect the error before to do the deformation analysis and prediction of the deformation object.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2016年第11期1295-1299,共5页
Journal of Liaoning Technical University (Natural Science)
基金
国家自然科学基金项目(41374007)
测绘地理信息江西省研究生创新教育基地项目(2310700008)
