Comparative Analysis of Machine Learning and Geostatistical Approaches for GNSS-InSAR Integration: A Case Study in Anatolia

The main focus of the study is to calibrate Sentinel-1 InSAR Line-of-Sight (LOS) velocities along a ~700 km North-South transect extending from the Black Sea coast (Kastamonu-Samsun) to the Mediterranean (Mersin-Gaziantep). This transect encompasses diverse tectonic regimes, including the North Anatolian Fault Zone, the Central Anatolian Block, and the junction of the East Anatolian Fault Zone. This complex structure of the transect requires detailed analysis of the GNSS-InSAR calibration procedure including validation. 

Across the study region, processed LiCSAR products are integrated with 3D velocities derived from the continuous local CORS network (21 stations) and an extensive campaign-based GNSS network (200 stations). For calibration, GNSS velocities are first projected into the satellite LOS geometry using LOS vectors derived from coherent InSAR pixels within a 1-km radius. The velocity bias (ΔVlos) is calculated at continuous GNSS locations. This correction surface is propagated using various conventional and Machine Learning techniques independently, including Kriging, Weighted Least Squares (WLS) based Quadratic Surface fitting, Thin Plate Spline (TPS) and Radial Basis Functions (Gaussian, Multiquadric, and Inverse Multiquadric). To address specific error sources, the contributions of topography-correlated atmospheric delays and local spatial trends are also analyzed by Geographically Weighted Regression (GWR) and Random Forest regression. Cross-validation is applied to assess the quality of each model individually where spatial random sampling and plate boundaries are also considered. This study presents preliminary results for obtaining a validated basis for generating up-to-date velocity fields over Türkiye.