Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter

Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalma...

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Published in:	ISPRS international journal of geo-information Vol. 5; no. 12; p. 236
Main Authors:	Dai, Wujiao, Liu, Ning, Santerre, Rock, Pan, Jiabao
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.12.2016
Subjects:	Algorithms Covariance dam deformation Data analysis Deformation Kalman filters kriging Kriging interpolation Monitoring Noise Noise prediction prediction space and time Space-Time Kalman filter spatio-temporal interpolation and prediction
ISSN:	2220-9964, 2220-9964
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Abstract	Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal filtering model, is used as a spatio-temporal data analysis method for dam deformation. There were three main steps in the method applied in this paper. The first step was to determine the Kriging spatial fields based on the characteristics of dam deformation. Next, the observation noise covariance, system noise covariance, the initial mean vector state, and its covariance were estimated using the Expectation Maximization algorithm (EM algorithm) in the second step. In the third step, we filtered the observation noise, interpolated the whole dam unmonitored data in space and time domains, and predicted the deformation for the whole dam using the Kalman filter recursion algorithm. The simulation data and Wuqiangxi dam deformation monitoring data were used to verify the STKF method. The results show that the STKF not only can filter the deformation data noise in both the temporal and spatial domain effectively, but also can interpolate and predict the deformation for the whole dam.
AbstractList	Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal filtering model, is used as a spatio-temporal data analysis method for dam deformation. There were three main steps in the method applied in this paper. The first step was to determine the Kriging spatial fields based on the characteristics of dam deformation. Next, the observation noise covariance, system noise covariance, the initial mean vector state, and its covariance were estimated using the Expectation Maximization algorithm (EM algorithm) in the second step. In the third step, we filtered the observation noise, interpolated the whole dam unmonitored data in space and time domains, and predicted the deformation for the whole dam using the Kalman filter recursion algorithm. The simulation data and Wuqiangxi dam deformation monitoring data were used to verify the STKF method. The results show that the STKF not only can filter the deformation data noise in both the temporal and spatial domain effectively, but also can interpolate and predict the deformation for the whole dam.
Author	Liu, Ning Dai, Wujiao Santerre, Rock Pan, Jiabao
Author_xml	– sequence: 1 givenname: Wujiao surname: Dai fullname: Dai, Wujiao – sequence: 2 givenname: Ning orcidid: 0000-0002-1038-5659 surname: Liu fullname: Liu, Ning – sequence: 3 givenname: Rock surname: Santerre fullname: Santerre, Rock – sequence: 4 givenname: Jiabao orcidid: 0000-0003-0151-3089 surname: Pan fullname: Pan, Jiabao
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SubjectTerms	Algorithms Covariance dam deformation Data analysis Deformation Kalman filters kriging Kriging interpolation Monitoring Noise Noise prediction prediction space and time Space-Time Kalman filter spatio-temporal interpolation and prediction
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Title	Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter
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