In this thesis I present 3 different works developed during the PhD. These three works are already published.
My research has been focused on onsite EEW techniques oriented to the seismic risk reduction for buildings. As matter of fact, in the first work (Iaccarino et al., 2020; Chapter 2), "Onsite earthquake early warning: Predictive models for acceleration response spectra considering site effects" , we presented an EEW method that predict Response Spectra of Acceleration (RSA) at nine different periods from P-wave parameters (i. e., Pd and IV2) on 3s window. RSA is a ground motion parameter of particular interest for structural engineers since it better correlates with structural damage than peak parameters such as PGA and PGV (Elenas and Meskouris, 2001). To account for site-effects, we retrieved a partially non-ergodic model using a mixed-effect regression analysis. This procedure helped us to reduce the prediction uncertainty. Finally, we analyzed the correction terms by station, and we found that the stations with the more positive ones (grater RSA) were the same stations to have amplification effects highlighted by H/V analysis. Furthermore, our models improve the EEW performances both in terms of true negatives and false positives.
The second work I present, "Earthquake Early Warning System for Structural Drift Prediction using Machine Learning and Linear Regressors" (Iaccarino et al., 2021; Chapter 3), uses data recorded from in-building sensors from Japanese and Californian structures. Here, we developed a method to predict Structural Drift using P-wave features (i. e., Pd, IV2, and ID2) from 1s, 2s, and 3s windows. We studied the effects of the complexity of the dataset on the predictions subdividing the Japanese dataset in three subsets: data from one building; data from buildings with the same material of construction; entire dataset. From this study, we found that the variability of the dataset plays a key role in the predictions increasing the uncertainties of the predictions for the complete dataset. Moreover, we compared the performances of linear least square models and non-linear machine learning regressors finding that the last ones perform always better. In the end, we tried to export the model retrieved on Japanese buildings to the Californian buildings, finding that the drift predictions are underestimated by a bias. We proposed to correct this bias using magnitude dependent correction terms, finding that the linear models are more able to adapt in these conditions.
In the end, I present "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network" (Chapter 4; Picozzi and Iaccarino, 2021). Here, we used catalogue information from a very complete dataset of the Californian geothermal area, The Geysers. From the catalogue, we chose 8 events with M>=3.9, and we selected the first 5 as training set and rest as testing set. Then, we extracted 9 features as time-series: the b-value and completeness magnitude, Mc, of the Gutenberg-Richter law; the fractal dimension of hypocenters, Dc; the generalized distance between pairs of earthquakes, η; the Shannon's information entropy, h; the moment magnitude, Mw, and moment rate, M ̇_0; the total duration of event groups, ΔT, and the inter-event time, Δt. We wanted to assess the possibility to detect changes in time of these features that can be related to deviations from the background seismicity. We built two Recurrent Neural Networks, one to detect preparatory phase the other to detect the aftershocks phase. The method is able to discriminate both the preparatory phase and the aftershock phase on the testing set. In the end, merging the predictions of two methods, we found that all the three events in testing set present a preparatory phase that lasts from 4 hours to 2 days before the main event
