Back to Search Start Over

Non-Linear PSInSAR Analysis of Deformation Patterns in Islamabad/Rawalpindi Region: Unveiling Tectonics and Earthquake-Driven Changes.

Authors :
Afzal, Zeeshan
Balz, Timo
Asghar, Aamir
Source :
Remote Sensing. Apr2024, Vol. 16 Issue 7, p1194. 18p.
Publication Year :
2024

Abstract

The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes. To address this limitation, we use a non-linear PSInSAR technique, which is an enhancement of PSInSAR, to identify non-linear deformation patterns. We processed Sentinel-1A images from ascending and descending orbits in the Islamabad/Rawalpindi region from December 2015 to January 2023 using non-linear PSInSAR. By calculating the differences in deformation, we analyzed surface movements and assessed the impact of the 2017 earthquake on urban areas. Our findings reveal that the earthquake significantly increased the deformation in ascending and descending orbit tracks, with an average deformation of up to 70 mm/yr and a line-of-sight movement of up to 30 mm/yr. Our observations indicate that the deformation is directed towards the line of sight in the north and south of the deformed area, suggesting subsidence between the two uplifting faults, potentially linked to a concealed fault line along the deformation zone boundary. This contradicts previous arguments, suggesting that water extraction is the leading cause of deformation. Our analysis with non-linear PSInSAR demonstrates that tectonics play a significant role in deformation, providing valuable insights into tectonic-activity-induced deformations in urban areas over the long term. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
7
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
176594832
Full Text :
https://doi.org/10.3390/rs16071194