A New Machine Learning Method for Advanced Treatment of InSAR Deformation Data: Preliminary Results from the Guadalentín Basin (Spain)

The processing and analysis of the large volumes of data generated by Interferometric Synthetic Aperture Radar (InSAR) require a significant investment of time, particularly in regions with complex geodynamic behavior. While InSAR presents notable advantages in terms of spatial coverage, precision, or data acquisition speed, traditional analytical methods can be insufficient to fully capture the complexity of deformation patterns or to efficiently manage the increasing amount of available data.

Integrating machine learning techniques into the InSAR computations and interpretation workflow enhances efficiency and automation. These methods enable automated detection of deformation patterns, improved separation of geophysical signals from atmospheric or orbital noise, and the identification of subtle or non‑linear ground motion that may be overlooked by conventional approaches. Such capabilities provide a more robust, reproducible, and sensitive framework for deformation analysis, which is essential for subsequent inversion procedures.

We describe in this presentation first results obtained in the Guadalentin Basin (SE Spain) using all these combined methodologies, as well as the comparison with previous studies for the area.