经验模态分解在高层建筑GPS变形监测分析中的应用

Application of Empirical Mode Decomposition in GPS Deformation Monitoring Analysis of High Rise Building

经验模态分解是一种数据驱动的自适应分解方法,能够把数据信号分解为具有自然物理意义的内蕴模态函数,有效分解各个周期的变形信号,相对小波分析等方法有其固有优势。本文针对某高层建筑物的GPS变形监测时间序列,采用EMD分解方法分离得到了建筑物的日周期、半日周期、整体趋势等多个方面振动特征,符合实际变形信号特征,验证了该方法在变形监测分析中的实用性。

Empirical mode decomposition is a data driven adaptive decomposition method,which can effectively de-compose the data signals into intrinsic mode functions with natural physical meaning,and get multi-period cycles. It has inherent advantages relative to the wavelet analysis method. In this paper,take the GPS deformation monitoring time se-ries of high-rise buildings as example,the EMD decomposition method is used to get the vibration features in the diurnal, semidiurnal period,and the linear trend,which is in accordance with the actual deformation characteristic signal. That verify the practical applications of EMD method in the deformation monitoring and analysis.