## 4 Simulation of the Seismicity  
By means of a newly developed version of our physically based simulation code Console et al. (2020) and references therein), we compiled synthetic earthquake catalogs lasting 100,000 years for events of magnitude \(\geq\) 4.2 within the polygonal area depicted in Figure 1. In this version, no application is performed on the State and Rate formulation, but we adopted an enhanced role of the static Coulomb stress transfer between every ruptured element of the fault model and all the other elements in the surrounding faults. In this new version of the code, the magnitude distribution of the simulated catalog is controlled by two free parameters to be selected by the user ([PERSON] et al. (2020); [PERSON] et al. (2020)):  
* The strength-reduction coefficient (S-R); this coefficient controls the growth of an initiated rupture, reducing the strength that must be exceeded for rupturing new elements of the expanding rupture, as a proxy of weakening mechanism;
* The aspect-ratio coefficient (A-R); this coefficient limits the progress of strength reduction if the ruptured area exceeds a given number of times the square of the width of the rupturing fault system, discouraging rupture propagation over very long distances. The seismogenic model adopted in the simulation algorithm is depicted in Figure 1, and the slip rates assumed for each fault segment are the highest values of the range reported by the DISS database (DISS Working Group (2019); Table S1 of the Supplementary Material). We carried out a set of tests to investigate the effect that the two above described free parameters have on the magnitude distribution of the output catalogs, letting the S-R parameter assume the values 0.1, 0.2, and 0.3, and the A-R parameter the values 2, 5, and 10, respectively. The results of these tests are reported in Figure S1 of the Supplementary Material. Each 100,000 years catalog was divided in 270 groups of 370 years (with the purpose of simulating many instances of the real catalog), counting the number of multiplets contained in each of them. Table 3 reports the averages and the standard deviation for the 270 elements population. Then, for each of the nine cases, we carried out the same analysis on 50 randomized catalogs obtained from the 100,000 years original ones by shuffling the origin time of all earthquakes by a random permutation. Finally, the average and the standard deviation of the ratios between the total number of multiplets in the original catalogs and those obtained from the respective randomized catalogs was computed (Table 4). On the basis of the above-mentioned tests, although the largest number of multiplets is provided by the couple of parameters 0.1 and 2, we chose the simulation obtained with the values 0.2 and 10 for the S-R and A-R parameters, respectively, which gives the largest ratio of multiplets. Figure 3 shows the results of this simulation with the 13,845 earthquakes having \(M_{w}\geq\) 5.0, evidencing the fault segments where the number of simulated earthquakes is higher. Our simulation algorithm does not produce any seismic activity outside the borders of the faults considered in the seismogenic model. A comparison of seismic features detected in the CPTI15 catalog, in the time interval 1650-2020, and the 100,000 years simulated catalog for the study area is shown in Table 5. For example, in this table we can compare the rate of earthquakes with \(M_{w}\geq\) 5.0 in the simulated catalog (0.138/yr) with the corresponding rate of earthquakes with \(M_{w}\geq\) 5.0 in the real catalog (0.573/yr). This circumstance is justified by the adoption of relatively high values of the S-R and A-R free parameters, which favor the growth of nucleated ruptures and accordingly produce a relatively large quantity of strong earthquakes. Moreover, we should take into account the fact that the source model adopted in our simulation does not include the numerous small sources, capable of producing only \(M_{w}\leq\) 5.5 earthquakes.