Self-organizing-Map Analysis of InSAR Time Series for the Early Warning of Structural Safety in Urban Areas

Among the many causes of collapse of civil structures, those related to the downfall of foundations are crucial for their likely catastrophic consequences. Interferometric synthetic aperture radar (InSAR) techniques may help monitoring the time evolution of ground displacements affecting engineered structures in large urban areas. Artificial neural networks can be exploited to analyze the huge amount of data that is collected over long periods of time on very dense grid of geographical points. The paper presents a neural network-based analysis tool, able to evidence similarities among time series acquired in different points and times. This tool could support an early-warning system, aiming to forecast critical events in urban areas. The implemented procedure is tested on a dataset of InSAR time series recorded over an area of the city of London.