AGU Fall Meeting, San Francisco CA, USA, 10-14 December
P. Martin Mai, Jan Burjanek, Bertrand Delouis, Gaetano Festa, Caroline Francois-Holden, Damiano Monelli, Takahiko Uchide,
Jiri Zahradnik
Images of earthquake ruptures, obtained from modelling/inverting seismic and/or geodetic data exhibit a high degree in spatial complexity. This earthquake source heterogeneity controls seismic radiation, and is determined by the details of the dynamic rupture process. In turn, such rupture models are used for studying source dynamics and for ground-motion prediction. But how reliable and trustworthy are these earthquake source inversions? Rupture models for a given earthquake, obtained by different research teams, often display striking disparities (see http://www.seismo.ethz.ch/srcmod) However, well resolved, robust, and hence reliable source-rupture models are an integral part to better understand earthquake source physics and to improve seismic hazard assessment. Therefore it is timely to conduct a large-scale validation exercise for comparing the methods, parameterization and data-handling in earthquake source inversions.We recently started a blind test in which several research groups derive a kinematic rupture model from synthetic seismograms calculated for an input model unknown to the source modelers. The first results, for an input rupture model with heterogeneous slip but constant rise time and rupture velocity, reveal large differences between the input and inverted model in some cases, while a few studies achieve high correlation between the input and inferred model. Here we report on the statistical assessment of the set of inverted rupture models to quantitatively investigate their degree of (dis-)similarity. We briefly discuss the different inversion approaches, their possible strength and weaknesses, and the use of appropriate misfit criteria. Finally we present new blind-test models, with increasing source complexity and ambient noise on the synthetics. The goal is to attract a large group of source modelers to join this source-inversion blindtest in order to conduct a large-scale validation exercise to rigorously asses the performance and reliability of current inversion methods and to discuss future developments.