EGU2020-5150
https://doi.org/10.5194/egusphere-egu2020-5150
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Stochastic generator of earthquakes in French territories

Corentin Gouache1, Pierre Tinard2, François Bonneau1, and Jean-Marc Montel3
Corentin Gouache et al.
  • 1Université de Lorraine, CNRS, GeoRessources, France (corentin.gouache@univ-lorraine.fr)
  • 2Caisse Centrale de Réassurance, R&D Modélisation, France
  • 3Université de Lorraine, CRPG/CNRS, France

Both French mainland and Lesser Antilles are characterized by sparse earthquake catalogues respectively due to the low-to-moderate seismic activity and the low recording historical depth. However, it is known that major earthquakes could strike French mainland (e.g. Ligure in 1887 or Basel in 1356) and even more French Lesser Antilles (e.g. Guadeloupe 1943 or Martinique 1839). Assessing seismic hazard in these territories is necessary to support building codes and prevention actions to population. One approach to estimate seismic hazard despite lack of data is to generate a set of plausible seismic scenarios over a large time span. A generator of earthquakes is thus presented in this paper. Its first step is to generate only main shocks. The second step consists of trigger aftershocks related to main shocks.
To draw the time occurrence of main shocks, original draw of frequencies and year-by-year summation of it is proceeded. The frequencies are drawn, for each magnitude step, in probability density functions computed through the inter event time method (Hainzl et al. 2006). By propagating magnitude uncertainties contained in the initial catalogue through a Monte Carlo Markov Chain, each magnitude step has not only one main shock frequency but a distribution of it. Once a main shock is temporally drawn, its 2D location is drawn thanks to the cumulative seismic moment recorded on each 5x5 km cell in the French territories. A seismotectonic zoning is used to limit both the spatial distribution and magnitude of large earthquakes. Finally, the other parameters (strike, dip, rake and depth) are drawn in ranges of values depending on the seismotectonic zone where the main shock is located. 
For purpose of trigger aftershocks from the main shocks, an approximation of the Bath law (Richter 1958; Båth 1965) is proceeded during the computation of the frequency – magnitude distributions. Thus, for each magnitude step, an α–value distribution is obtained in which, for each main shock an α–value is drawn. In this way, the maximal magnitude of triggered aftershocks is known.

How to cite: Gouache, C., Tinard, P., Bonneau, F., and Montel, J.-M.: Stochastic generator of earthquakes in French territories, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5150, https://doi.org/10.5194/egusphere-egu2020-5150, 2020

Comments on the presentation

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Presentation version 1 – uploaded on 29 Apr 2020
  • CC1: Comment on EGU2020-5150, Angela Stallone, 04 May 2020

    Could you explain in more detail how you estimate the mainshock proportion (parameter ‘gamma’ in slide 4)?

    • AC1: Reply to CC1, Corentin Gouache, 04 May 2020

      Thank you for this comment.
      From a cut-off magnitude and for each magnitude step (0.1) we compute the main shock proportion as the reverse of the normalized inter event times varaince (Molchan, 2005) + an added term (Hainzl et al., 2006). However when we use this procedure for the largest magnitudes (around Mw>=6 in our study case) results start to be inaccurated (at least incoherent) becaused of data scarcity.
      We thus try to overcome this drawback by stabilizing the computed main shock proportions for large magnitudes. To do that, we use an uncertainty threshold (red line on slide 4). This one has been set to 1 - 0.1 = 0.9 in our study. From the first magnitude (Mw_first on slide 4) associated with main shock proportion >= 0.9, we assume that all the proportions must be >= 0.9. If it's the case, nothing is done and results are kept. If one or several main shock proportion(s) is/are under 0.9 then this/these proportion(s) is/are automatically set to 1.
      Without this implementation, the median main shock proportion grows up to reach 1 at Mw5.8 then decreases up to 0.6 from Mw6 to the maximal magnitude (around 7).
      This approach seems to be conservative because sets proportion to 1 when it's unkown but our results are consistent comparing with a classical declustering algorithm (Grünthal, 2009).

      • CC2: Reply to AC1, Angela Stallone, 04 May 2020

        And what you get is that the probability for an earthquake to be a mainshock increases with its magnitude. I see, thank you Corentin

        • AC2: Reply to CC2, Corentin Gouache, 04 May 2020

          That's it! We asume an increasing main shock proportion with the magnitude and this implementation allows it.