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Inversion of InSAR data via Genetic Algorithm and Simulated Annealing methods: first results from the Campi-Flegrei volcanic region

Manoochehr Shirzaei(1), Thomas R. Walter(1), Mahdi Motagh(1), Andrea Manconi(1) and Riccardo Lanari(2)

(1) GeoForschungsZentrum (GFZ), VolcanoTectonics Junior Research Group, 14473 Potsdam, Germany
(2) IREA, Via Diocleziano, 328-80124 Napoli, Italy


Fast source characterization is of major importance in volcano and earthquake monitoring efforts. We have applied the Genetic Algorithm (GA) and Simulated Annealing (SA) methods to the inversion of InSAR derived surface deformation data associated with volcanic and tectonic activities. We augment these methods with a statistical approach to estimate the confidence interval of the searched parameters, considering their interaction, the effect of the model deficiency, as well as the observational error. To prevent SA algorithm from getting trapped in a local minima we use a random search method to increase redundancy for exploring search space. Additionally, we derive mathematical equations that directly relate individual parameters of the magma chamber to the surface deformation data which can reduce computational time of convergence in the inversion. To evaluate the efficiency and reliability of the inversion methods presented in this work, we first used synthetic data sets. Then we applied these inversion techniques to two sets of SBAS-InSAR data from the Campi-Flegrei volcanic region in Italy. We consider the uplift period 2000-2001 and subsidence period 2001-2002. Inversion of these two data sets allows deriving source parameters of the deformation and comparison of the two inversion methods.


Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry