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A clustering analysis of three dimensional ground deformation map estimated by integrating DInSAR and GPS dataset

Alessandro Spata, Giuseppe Nunnari, Giuseppe Puglisi, Francesco Guglielmino, Alessandro Bonforte, Placido Montalto
In this paper a clustering analysis based on the combination of the Self-Organizing Map (SOM) and the K-means method is applied to three dimensional ground deformation map obtained by integrating sparse Global Positioning System (GPS) and Differential Interferometric Synthetic Aperture Radar (DInSAR) acquired at Mt Etna in the period 2003-2004. This analysis is aimed to partition the whole displacement field into subsets sharing some common displacements features in order to recognize and classify deformation patterns affecting different sectors of Etna volcano.
Results have been also confirmed by a fuzzy c-mean analysis.
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