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Proceedings Paper

Neural Networks for automatic seismic source analysis from DInSAR data
Author(s): Matteo Picchiani; Fabio Del Frate; Giovanni Schiavon; Salvatore Stramondo; Marco Chini; Christian Bignami
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Paper Abstract

Satellite SAR Interferometry (InSAR) has already proved its effectiveness in the analysis of seismic events. In fact, measuring the surface displacement field generated by an earthquake can be useful to define fault parameters regarding the geometry (such as dip and strike angles, width, length), the extension of the rupture and the distribution of slip on the fault plain. However, to retrieve the source parameters from InSAR measurements is rather complex since the inversion problem is ill-posed. In this work we propose an inversion approach for retrieving the fault parameters based on neural networks, trained by simulated data sets generated by means of the Okada forward model. The developed work-flow implements a pre-processing step, aiming to reducing the data dimensionality, in order to improve the performance of the neural network inversion. The methodology has been validated by using experimental data sets obtained using different wavelength and representative of different kind of seismic source mechanisms.

Paper Details

Date Published: 27 October 2011
PDF: 7 pages
Proc. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, 81790K (27 October 2011); doi: 10.1117/12.898575
Show Author Affiliations
Matteo Picchiani, Univ. degli Studi di Roma Tor Vergata (Italy)
Fabio Del Frate, Univ. degli Studi di Roma Tor Vergata (Italy)
Giovanni Schiavon, Univ. degli Studi di Roma Tor Vergata (Italy)
Salvatore Stramondo, Istituto Nazionale di Geofisica e Vulcanologia (Italy)
Marco Chini, Istituto Nazionale di Geofisica e Vulcanologia (Italy)
Christian Bignami, Istituto Nazionale di Geofisica e Vulcanologia (Italy)


Published in SPIE Proceedings Vol. 8179:
SAR Image Analysis, Modeling, and Techniques XI
Claudia Notarnicola; Simonetta Paloscia; Nazzareno Pierdicca, Editor(s)

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