Automatic Classification of Active Deformation Areas Based on Synthetic Aperture Radar Data and Environmental Covariates Using Machine Learning—Application in SE Spain

Abstract

Deformation processes, both natural (e.g., subsidence, landslides, active tectonics) and induced (e.g., associated with mining, construction. groundwater exploitation), result in significant socioeconomic losses worldwide. Accurate detection and classification of these processes are crucial for effective risk management. In this study, we present a novel approach for the automatic classification of deformation processes using Interferometric Synthetic Aperture Radar (InSAR) data and machine learning techniques. Specifically, we use a decision tree-based classification algorithm to train a model capable of recognizing and distinguishing different types of deformation processes using time series of displacements, grouped into Active Deformation Areas (ADAs). We test this methodology in a large area in SE Spain. Our results demonstrate promising performance, with an Area Under the Curve (AUC) > 0.95, identifying several covariates of morphometric, geological, hydrogeological, and geotechnical nature as key factors. This automatic classification of InSAR data holds significant implications for risk management associated with ground deformation, providing a potentially valuable tool for decision makers in urban planning and land management officials.

Keywords

• geohazards
• Displacement Time Series
• SAR
• ADAs
• Time Series Clustering
• machine learning