Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 19
Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6903-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6903-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 19
Development and technical paper
 | 
08 Oct 2025
Development and technical paper |  | 08 Oct 2025

Enhanced land subsidence interpolation through a hybrid deep convolutional neural network and InSAR time series

Zahra Azarm, Hamid Mehrabi, and Saeed Nadi

Viewed

Total article views: 1,296 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
970 283 43 1,296 37 77
  • HTML: 970
  • PDF: 283
  • XML: 43
  • Total: 1,296
  • BibTeX: 37
  • EndNote: 77
Views and downloads (calculated since 04 Jun 2024)
Cumulative views and downloads (calculated since 04 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,296 (including HTML, PDF, and XML) Thereof 1,296 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Oct 2025
Download
Short summary
The article introduces a new method to estimate land subsidence using deep convolutional neural networks (CNNs) and persistent scatterer interferometric synthetic aperture radar (PSInSAR), addressing the limitations of traditional methods. It focuses on Isfahan Province, Iran, and demonstrates substantial improvement over conventional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.
Read more
Share