Reconstruction of Spatially Continuous Time-Series Land Subsidence Based on PS-InSAR and Improved MLS-SVR in Beijing Plain Area - 2023

Geological, Geophysics, Environmental & Geotechical Engineering

Mingyuan Lyu, and 10 co-authors reported in July 2023 that Beijing has undergone severe settlement (subsidence) in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges:

1) the nonlinear characteristics of time-series subsidence has not been fully investigated;, and

2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas.

To address the challenges, the authors presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then
used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then timeseries
deformation considering 40 features including thickness of the compressible layers, annual
groundwater level, etc.

The major results showed that:

1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2≈0.99), suggesting obvious nonlinear temporal pattern of deformation; and

2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods.

The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, groundwater-level datasets are the most influential predictors of time-series deformation.