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Abstract

In a probabilistic seismic hazard assessment (PSHA) the estimated likelihood of rare/large ground-motions is controlled by the aleatory variability of the Ground-Motion Model (GMM). This variability is partly from the poor modelling of earthquake physics, and partly from the poor quality of GMM parameters. Here, we investigate the impact of the definition of moment magnitude (M_W) on GMM variability. Indeed, M_W can either be estimated directly from the moment-tensor inversion or deduced from other magnitude scales. However, for the same event, even the moment-tensor based M_W estimates provided by different agencies often differ – due to differences in computation methods, inverted data, and network configuration. Various strategies have been proposed to define a unique M_W value for each earthquake. In this study, we developed Fourier Spectrum GMMs using the pan-European Engineering Strong Motion database using two disctinct strategies to define M_W: (1) the EMEC approach (ranking of the M_W sources) preferring M_W from event-specific studies, and (2) the IRSN approach (ranking + unification) in which all M_W values are unified with those from the Global and Regional Centroïd Moment Tensor catalogs. We observe that the IRSN unified M_W achieves a 18% smaller between-event variability at low frequencies (e.g. 0.3Hz) compared to EMEC M_W. Such reduction in GMM variability makes a strong case for the use of M_W unification approaches in GMMs and in earthquake catalogs – the two blocks of PSHA.