GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 1.1 GNGTS 2024 with broadband seismometers (Guralp CMG-40 T), with a fat response between 50 Hz and 60s. Contnuous waveform data have been archived as 1-day MSEED fles at DIAS (Dublin Insttute for advanced Studies). Our study focuses on the detecton and locaton procedure through a Markov chain Monte Carlo approach of both natural and human-induced seismicity recorded from August 2012 to July 2015 by 12 broadband seismic statons. We compile a frst manual-revised catalog of Donegal micro- seismicity, and we integrate it with the locaton of the seismic events occurred in a small seismic sequence during January 2012 in the study area. In order to detect the seismic events, we analysed the contnuous waveforms by applying a STA/ LTA network coincidence trigger algorithm (Team, 2017). Then, we performed a manual picking of P and S waves frst arrival tmes of the events detected by the trigger algorithm (Goldstein and Snoke 2005) 2005). Finally, a total of 114 earthquakes were located using a hierarchical Bayes Markov chain Monte Carlo (McMC) algorithm, developed on purpose for this study. The Markov chain Monte Carlo method allows us to estmate the realistc uncertaintes on the investgated parameters (Lomax et al., 2000). The strength of McMC method lies on the fact that the data uncertaintes are also considered as part of the unknowns and are robustly estmated through the McMC sampling following a Hierarchical Bayes approach (Malinverno and Briggs 2004). Moreover, a detailed velocity model of the investgated area does not exist at the moment. This aspect can be easily solved by using our method because we just need to specify the minimum and maximum values of all the priors to defne the prior probability distributon (i.e. the velocites of P and S waves). For a more precise compilaton of a seismic catalog, we also calculated the local magnitudes (ML) of the natural seismic events. We decided to use two diferent approaches for magnitude comparison due to scarceness of local seismicity and the consequent difcult calibraton of existng magnitude values with respect to ours. We frst used the classical formula for calculatng the individual magnitude at each staton provided by Richter (1935) that we call “MLRI352” in Table 2. For comparison, we then calculated the magnitude at each staton using a calibrated local scale for Ireland, provided in the study of Grannell et al., 2018, where they used a staton correcton coefcient and a distance-dependant term accountng for geometrical spreading and anelastc atenuaton, reported in Table 2 under the name “ML_Donegal”. We added a staton correcton term for the individual statons used by the Irish Seismic Network for both formulas. We then averaged the individual valued of ML resulted from each staton discarding the single values that were more than two standard deviatons from the mean ML value and we recalculated the fnal values of ML for each seismic event, as reported in Table 2. RESULTS We tested the performance of McMC algorithm statstcally through frequency distributon histograms reported in Figure 1, where we chose a “reference event” from the list of located events. These histograms show the gaussian distributon of the eight parameters used to describe the mathematcal model for the geophysical process of seismic wave propagaton and thus the