EGU21-3755
https://doi.org/10.5194/egusphere-egu21-3755
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Optimization of the time series surface deformation analysis using machine learning algorithms on the interferogram simulation data

Muhammad Fulki Fadhillah1, SeulKi Lee1, and Chang-Wook Lee2
Muhammad Fulki Fadhillah et al.
  • 1Departement of Smart Regional Innovation, Kangwon National University, Chuncheon-si, Korea, Republic of
  • 2Departement of Science Education, Kangwon National University, Chuncheon-si, Korea, Republic of

Time-series InSAR techniques, such as Stanford Method for Persistent Scatterers (StaMPS) are commonly used to measure time-series surface deformation. This study presents a novel approach of optimized time series deformation analysis based on a support vector regression (SVR) algorithm and optimization Hot-Spot Analysis on persistent scatterers (PS). To examine the performances of the optimized process in time-series, we generated a synthetic interferogram using a Mogi model equation to construct a simulated surface deformation phase. Topography errors simulated orbital error and atmospheric error phases have been added to synthetic interferogram construction. All the synthetic interferogram based on Sentinel-1 SAR Image acquisition dates over Seoul, Korea. An SVR algorithm was used to find an optimum measurement point and reduce error points in time-series analysis. Then, the OHSA approach was implemented on the optimum measurement point through the analysis of Getis-Ord Gi* statistics. As the result, the optimization measurement point indicates refined results in the mean velocity deformation map and time-series graph. In addition, the detection accuracy can be improved by more than 10% with synthetic data. Then, the correlation coefficient between the optimization result and the deformation model shows a good correlation (> 0.8). Also, the standard deviation of time-series results can be reduced by more than 7% after optimizing the process. The proposed method is useful to detect a low deformation rate and can be implemented for several deformation cases.   

How to cite: Fadhillah, M. F., Lee, S., and Lee, C.-W.: Optimization of the time series surface deformation analysis using machine learning algorithms on the interferogram simulation data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3755, https://doi.org/10.5194/egusphere-egu21-3755, 2021.

Displays

Display file