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Automatic seismic source modeling of InSAR displacements

Authors :
Simone Atzori
Fernando Monterroso
Andrea Antonioli
Claudio De Luca
Nikos Svigkas
Francesco Casu
Michele Manunta
Matteo Quintiliani
Riccardo Lanari
Source :
International Journal of Applied Earth Observations and Geoinformation, Vol 123, Iss , Pp 103445- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

In this work we describe the implementation of a processing chain for a fully automatic modeling of the seismic source parameters and its slip distribution through the inversion of the InSAR displacements generated from the EPOSAR service. This processing chain consists of a suite of procedures and algorithms handling a sequence of steps: selection of the highest quality InSAR datasets, definition of the area of interest, image sampling, non-linear and linear inversions to get, respectively, the source geometry and its slip distribution. A set of side procedures and interfaces also allows an interactive refinement and the publication of results, consisting of scientific data and graphical outputs. The whole procedure has been developed, tested and validated by considering 100 events with magnitudes between 5.5 and 8.2, worldwide distributed and covering an exhaustive range of mechanisms and tectonic contexts.Main aim of this work is describing the implementation of the automatic modeling procedures, used to produce solutions in real time, already during the emergency phase. These sources, validated by experts before their publication, can be a reference for operational purposes and initial scientific analyses. The creation of this repository sets also the framework to store, out of the emergency time, more sophisticated solutions, manually revised and/or with peer-review quality.

Details

Language :
English
ISSN :
15698432
Volume :
123
Issue :
103445-
Database :
Directory of Open Access Journals
Journal :
International Journal of Applied Earth Observations and Geoinformation
Publication Type :
Academic Journal
Accession number :
edsdoj.7b94de38a07b4f8390b81fa4c8c512f5
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jag.2023.103445