A Novel D-InSAR and Offset Tracking Fusion Algorithm Based on Variance-Masked Mean Filtering

Abstract

Accurate ground deformation monitoring is crucial for understanding geophysical activities and mitigating disaster risks. D-InSAR and offset tracking are widely used but face limitations: D-InSAR struggles with phase unwrapping errors in large-scale deformation regions, while offset tracking lacks precision in small-scale deformation. This paper proposes a fusion algorithm based on variance-masked mean filtering to combine the strengths of both methods. The algorithm filters noise in gentle regions of the offset map and fuses refined results with D-InSAR to enhance accuracy. Applied to the 2023 Türkiye earthquake, the method effectively reduces noise, corrects unwrapping errors, and achieves superior accuracy in large-scale deformation scenarios such as earthquakes and landslides.