Revamping the GFZ Energy Magnitude computation procedure to establish a new service

Location and magnitude are the primary information released by any seismological observatory to characterize an earthquake. Nowadays, the size of large enough earthquakes are routinely measured in terms of released seismic moment (moment magnitude, Mw). Whereas events with Mw above about 5.5 populate seismological archives connected to global monitoring networks, the moment magnitude of smaller events require the analysis of regional and local dense networks, or the establishment of empirical relationships to convert other magnitude scales into Mw (e.g., local magnitude to moment magnitude conversions). Since Mw is constructed over a physical parameter, it does not saturate. Moreover, being the seismic moment connected to tectonic features such as fault area and the average slip, Mw became the reference magnitude for seismic hazard studies. Although Mw accomplishes perfectly the task of characterizing the earthquake size, it does not provide the most complete view about the earthquake strength since Mw is insensitive to changes in the rupture dynamics. An assessment of the amount of the seismic energy released by an event (energy magnitude Me) is allowing to complement Mw with a measure of the earthquake size more suitable to evaluate the earthquake shaking potential. Aiming at introducing soon a new real-time service providing Me for major earthquakes we are presenting in this study the results of benchmark tests against the procedure proposed by Di Giacomo et al., in 2008 [1] as well as the analysis performed on a larger data set including all major events available in the GEOFON catalogue with a published moment magnitude since 2011. The initial procedure has been translated to a python code within the Stream2segment package [2] and leveraging on EIDA and IRIS data services, more than 2000 station for ~5000 events since 2011 have been downloaded and processed. The large data set used and the real-time application pose new challenges, among them the teleseismic distance, the strongly unbalanced network and the real-time data flow making the data set used dynamic. We present and discuss here the effects of these complications and how we are tackling them towards the implementation of new service at GFZ computing Me in real-time. [1] Di Giacomo, D., Grosser, H., Parolai, S., Bormann, P., and Wang, R. (2008), Rapid determination of Me for strong to great shallow earthquakes, Geophys. Res. Lett., 35, L10308, doi:10.1029/2008GL033505. [2] Riccardo Zaccarelli, Dino Bindi, Angelo Strollo, Javier Quinteros, Fabrice Cotton; Stream2segment: An Open‐Source Tool for Downloading, Processing, and Visualizing Massive Event‐Based Seismic Waveform Datasets. Seismological Research Letters ; 90 (5): 2028–2038. doi: https://doi.org/10.1785/0220180314