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InSAR Data and Neural Networks applied to Seismic Source Quantitative Parameters Retrieval

Salvatore Stramondo(1), Fabio Del Frate(2) and Giovanni Schiavon(2)

(1) Istituto Nazionale di Geofisica e Vulcanologia, via di Vigna Murata 605, 00143 Rome, Italy
(2) Tor Vergata University, Via del Politecnico, 1, 00133 Rome, Italy


The basic idea of this work is to exploit the capabilities of neural networks: the quantitative modelling of the seismic source and the interferogram inversion for retrieving its geometric parameters. The problem can be sum up as follows. When a moderate-to-strong earthquake occurs we can apply SAR Interferometry (InSAR) technique to compute a differential interferogram. The latter is used to detect and measure the surface displacement field. The earthquake has been generated by an active, seismogenic, fault having its own specific geometry. Therefore each differential interferogram contains the information concerning the geometry of the seismic source the earthquake comes from; its shape and size, the number of fringes, the lobe orientation and number are the main features of the surface effects field. An artificial neural network is properly generated and trained to provide an inversion procedure applied to single out the geometric parameters of the fault. Based on the Okada algorithms we computed the forward models (the synthetic interferograms) by varying five fault parameters (fault Length and Width, fault Dip and Strike angle, Bottom Depth) within a certain range and step. The five-parameter inversion has been then applied to some test cases (Izmit earthquake, 1999; Bam earthquake, 2003) thus providing a reliable estimate of such parameters.


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