  Ground-motion prediction equations (GMPE) are essential in probabilistic
seismic hazard studies for estimating the ground motions generated by the
seismic sources. In low seismicity regions, only weak motions are available in
the lifetime of accelerometric networks, and the equations selected for the
probabilistic studies are usually models established from foreign data.
Although most ground-motion prediction equations have been developed for
magnitudes 5 and above, the minimum magnitude often used in probabilistic
studies in low seismicity regions is smaller. Desaggregations have shown that,
at return periods of engineering interest, magnitudes lower than 5 can be
contributing to the hazard. This paper presents the testing of several GMPEs
selected in current international and national probabilistic projects against
weak motions recorded in France (191 recordings with source-site distances up
to 300km, 3.8\leqMw\leq4.5). The method is based on the loglikelihood value
proposed by Scherbaum et al. (2009). The best fitting models (approximately
2.5\leqLLH\leq3.5) over the whole frequency range are the Cauzzi and Faccioli
(2008), Akkar and Bommer (2010) and Abrahamson and Silva (2008) models. No
significant regional variation of ground motions is highlighted, and the
magnitude scaling could be predominant in the control of ground-motion
amplitudes. Furthermore, we take advantage of a rich Japanese dataset to run
tests on randomly selected low-magnitude subsets, and check that a dataset of
~190 observations, same size as the French dataset, is large enough to obtain
stable LLH estimates. Additionally we perform the tests against larger
magnitudes (5-7) from the Japanese dataset. The ranking of models is partially
modified, indicating a magnitude scaling effect for some of the models, and
showing that extrapolating testing results obtained from low magnitude ranges
to higher magnitude ranges is not straightforward.
