Sensitivity analysis using the TREMOL code for seismicity forecasting&#160;
Authors: Monterrubio-Velasco Marisol , Zamora Natalia 
Published: March 2022

&lt;p&gt;Forecasting spatio-temporal occurrence of earthquakes is not a trivial step for the seismic and tsunami hazard assessments. Estimating earthquake rates depends on information of a nonlinear system that is poorly known including the source dimensions. Thus, these assessments rely on e.g. seismic catalogues, or geophysical and geological data that could portray the statistical and physical behaviour of the seismogenic zones. In particular, earthquakes could rupture along asperities or areas of the seismogenic zone with high stress accumulation.Those areas have different physical properties than the surrounding area, such as a high frictional strength and larger stress drop (e.g. Madariaga 1979, Corbi et al., 2017). In this work, we apply the TREMOL code (Monterrubio-Velasco et al., 2019), based on the Fiber Bundle Model, to validate it as a tool to reproduce the seismicity occurring by the rupture of large, in some cases, single asperities. We have selected four regions where large earthquakes have occurred: M8.8 Maule 2010 (Chile) earthquakes, M9.1 Tohoku 2011 (Japan), M7.6 Nicoya 2012 (Costa Rica) and M8.3 Coquimbo 2015 (Chile). In these tectonic regions, earthquake sequences are generated based on a discrete model of material failure used in TREMOL. One of the most notable results is that the maximum earthquakes of the real sequences are achieved. Also, in most cases, the magnitude - frequency distribution is similar to those of real data. While the outcomes of TREMOL are given in rupture areas, several area-magnitude scaling laws are employed to obtain moment magnitudes. By carrying out a sensitivity analysis of different scaling laws, we show the bias in the synthetic catalogues which is a critical input in seismic hazard assessment. It is shown that the synthetic seismicity using the Ramirez-Gaytan scaling law (Ramirez-Gaytan et al. 2014) is the best to fit the magnitude of the real series in most of the cases. Following the validation of TREMOL, we provide a new seismic scenario generator of future events to assist e.g. the Probabilistic Seismic/Tsunami Hazard Assessment (PSHA/PTHA) complementing the seismic forecast with other well known statistical tools.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Corbi, F., Funiciello, F., Brizzi, S., Lallemand, S., and Rosenau, M. (2017). Control of asperities size and spacing on seismic behavior of subduction megathrusts, Geophys. Res. Lett., 44, 8227&amp;#8211; 8235, doi:10.1002/2017GL074182.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Madariaga, R. (1979). On the relation between seismic moment and stress drop in the presence of stress and strength heterogeneity, J. Geophys. Res.-Sol. Ea., 84, 2243&amp;#8211;2250.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Monterrubio-Velasco et al., (2019). A stochastic rupture earthquake code based on the fiber bundle model (TREMOL v0.1): application to Mexican subduction earthquakes. Geosci. Model Dev., 12, 1809&amp;#8211;1831.&lt;/p&gt;&lt;p&gt;Ram&amp;#237;rez-Gayt&amp;#225;n, A., Aguirre, J., Jaimes, M. A., and Hu&amp;#233;rfano, V. (2014). Scaling relationships of source parameters of M w 6.9&amp;#8211;8.1 earthquakes in the Cocos&amp;#8211;Rivera&amp;#8211;North American subduction zone, Bulletin of the Seismological Society of America, 104, 840&amp;#8211;854.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;