Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence

Abstract

The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory.

Keywords

• Sentinel-1
• InSAR
• geohazards
• Random Forest
• Subsidence
• Landslide