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ISPRS Int. J. Geo-Inf. 2016, 5(12), 236; doi:10.3390/ijgi5120236

Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter

1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Center for Research in Geomatics, Laval University, Quebec, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Zhao-Liang Li, Jose A. Sobrino, Chao Ren and Wolfgang Kainz
Received: 4 September 2016 / Revised: 9 November 2016 / Accepted: 23 November 2016 / Published: 8 December 2016
(This article belongs to the Special Issue Recent Advances in Geodesy & Its Applications)
View Full-Text   |   Download PDF [2747 KB, uploaded 8 December 2016]   |  

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. View Full-Text
Keywords: Space-Time Kalman filter; dam deformation; Kriging interpolation; spatio-temporal interpolation and prediction Space-Time Kalman filter; dam deformation; Kriging interpolation; spatio-temporal interpolation and prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dai, W.; Liu, N.; Santerre, R.; Pan, J. Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter. ISPRS Int. J. Geo-Inf. 2016, 5, 236.

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