Gross error detection and correction with deformation observation data based on local mean decomposition method

Abstract

針對變形監測數據中粗差探測與修復問題, 提出一種基于局部均值分解(LMD)的粗差探測方法, 并結合三次樣條插值方法對粗差點進行修復. 通過LMD方法對變形序列進行分解得到其PF分量, 根據高頻分量的奇異點確定可疑粗差點, 將分解分量去除高頻分量進行重構, 利用數學檢驗方法確定粗差點位置. 剔除粗差點后, 采用三次樣條插值方法進行修復粗差點. 研究結果表明: 局部均值分解方法在變形監測數據處理中的粗差探測效果明顯, 三次樣條插值修復也基本準確, 為大壩變形多尺度分析奠定了較好的基礎. 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.