scholarly journals Crustal Deformation Monitoring in Beijing Using Radarsat-2 InSAR Time Series Analysis

2018 ◽  
Author(s):  
L. Y. Hu ◽  
Y. S. Li ◽  
C. Q. Xing ◽  
K. R. Dai ◽  
R. Yan ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crustal Deformation ◽  
Deformation Monitoring ◽  
Series Analysis

scholarly journals GPS and InSAR Time Series Analysis: Deformation Monitoring Application in a Hydraulic Engineering Resettlement Zone, Southwest China

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ruya Xiao ◽  
Xiufeng He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Monitoring System ◽  
Land Surface ◽  
Southwest China ◽  
Deformation Monitoring ◽  
Hydraulic Engineering ◽  
Ground Movement ◽  
Series Analysis ◽  
Gps Monitoring

Booming development of hydropower in China has resulted in increasing concerns about the related resettlement issues. Both global positioning system (GPS) and persistent scatterer interferometric synthetic aperture radar (InSAR) time series analysis are applied to measuring the magnitude and monitoring the spatial and temporal variations of land surface displacement in Hanyuan, a hydraulic engineering resettlement zone, southwest China. The results from the GPS monitoring system established in Hanyuan match well the digital inclinometer results, suggesting that the GPS monitoring system can be employed as a complement to the traditional ground movement monitoring methods. The InSAR time series witness various patterns and magnitudes of deformation in the resettlement zone. Combining the two complementary techniques will overcome the limitations of the single method.

Recent advances in SAR interferometry time series analysis for measuring crustal deformation

2012 ◽  
Vol 514-517 ◽  
pp. 1-13 ◽  
Author(s):  
Andrew Hooper ◽  
David Bekaert ◽  
Karsten Spaans ◽  
Mahmut Arıkan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crustal Deformation ◽  
Sar Interferometry ◽  
Series Analysis ◽  
Recent Advances

scholarly journals Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yu Morishita
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Urban Areas ◽  
Ground Deformation ◽  
Processing System ◽  
Economic Loss ◽  
Deformation Monitoring ◽  
Ground Subsidence ◽  
Detection Mechanism ◽  
Series Analysis

AbstractGround subsidence in urban areas is a significant problem because it increases flood risk, damages buildings and infrastructure, and results in economic loss. Continual monitoring of ground deformation is important for early detection, mechanism understanding, countermeasure implementation, and deformation prediction. The Sentinel-1 satellite constellation has globally and freely provided frequent and abundant SAR data and enabled nationwide deformation monitoring through InSAR time series analysis. LiCSAR, an automatic Sentinel-1 interferometric processing system, has produced abundant interferograms with global coverage, and the products are freely accessible and downloadable through a web portal. LiCSBAS, an open source InSAR time series analysis package integrated with LiCSAR, enables users to obtain the deformation time series easily and quickly. In this study, spatially and temporally detailed deformation time series and velocities from the LiCSAR products using LiCSBAS for 73 major urban areas in Japan during 2014–2020 were derived. All LiCSBAS processing was automatically performed using predefined parameters. Many deformation signals with various temporal and spatial features, such as linear subsidence in Hirosaki, Kujyukuri, Niigata, and Kanazawa, episodic subsidence in Sanjo, annual vertical fluctuation in Hirosaki, Yamagata, Yonezawa, Ojiya, and Nogi, and linear uplift in Chofu were detected. Unknown small nonlinear uplift signals were found in Nara and Osaka in 2018. Complex postseismic deformations from the 2016 Kumamoto earthquake were also revealed. All the deformation data obtained in this study are available on an open repository and are expected to be used for further research, investigation, or interpretation. This nationwide monitoring approach using the LiCSAR products and LiCSBAS is easy to implement and applicable to other areas worldwide.

scholarly journals Deformation monitoring and prediction for residential areas in the Panji mining area based on an InSAR time series analysis and the GM-SVR model

2019 ◽  
Vol 11 (1) ◽  
pp. 738-749
Author(s):  
Jinchao Li ◽  
Fei Gao ◽  
Jiaguo Lu ◽  
Tingye Tao
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Mining Area ◽  
Deformation Monitoring ◽  
Dynamic Monitoring ◽  
Ground Subsidence ◽  
Support Vector ◽  
Residential Areas ◽  
Mining Areas ◽  
Series Analysis

Abstract Underground coal mining activities often cause ground subsidence and damage to surface construction, which seriously threatens the lives and property of residents in mining areas. In this paper, the deformation of the Yang Juzhuang village, which is a residential area in the Huainan mining area (China), was monitored through an interferometric synthetic aperture radar (InSAR) time series analysis. The vertical displacements were detected using thirteen Sentinel-1A images that were acquired between December 2016 and May 2017. The validity and applicability of the method are verified by comparing the acquired images with the GPS measurement results. Because of the deformation characteristics of the mining area, a prediction model that is combined with a grey support vector machine regression (GM-SVR) is proposed, and the practical effects of the model are verified using the deformation monitoring results of the study area. The combination of this model and SBAS-InSAR provides rapid dynamic monitoring and enables the issuance of disaster warnings in the region.

Research on Time Series Analysis Based Deformation Prediction Model

2011 ◽  
Vol 250-253 ◽  
pp. 2888-2891 ◽  
Author(s):  
Hao Zhang ◽  
Xi Shi ◽  
Li Fang Lai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Dynamic Deformation ◽  
Deformation Monitoring ◽  
Ar Model ◽  
Deformation Prediction ◽  
Series Analysis ◽  
Deformation Features ◽  
Dam Deformation

This paper introduces a method to apply time series analysis in dam deformation monitoring and prediction. We provide a simplified AR prediction model, which is relatively optimized in fitting constructive dynamic deformation features, analyzing deformation data and predicting deformation trend. We use this AR model in a certain dam’s deformation data processing, and prove it is an effective dynamic deformation prediction model.

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