Real-time seismology and earthquake damage mitigation

Real-time seismology refers to a practice in which seismic data are collected and analyzed quickly after a significant seismic event, so that the results can be effectively used for postearthquake emergency response and early warning. As the technology of seismic instrumentation, telemetry, computers, and data storage facility advances, the real-time seismology for rapid postearthquake notification is essentially established. Research for early warning is still underway. Two approaches are possible: (a) regional warning and (b) on-site (or site-specific) warning. In (a), the traditional seismological method is used to locate an earthquake, determine the magnitude, and estimate the ground motion at other sites. In (b), the beginning of the ground motion (mainly P wave) observed at a site is used to predict the ensuing ground motion at the same site. An effective approach to on-site warning is discussed in light of earthquake rupture physics

Seismic Risk Analysis of Revenue Losses, Gross Regional Product and transportation systems.

Natural threats like earthquakes, hurricanes or tsunamis have shown seri- ous impacts on communities. In the past, major earthquakes in the United States like Loma Prieta 1989, Northridge 1994, or recent events in Italy like L’Aquila 2009 or Emilia 2012 earthquake emphasized the importance of pre- paredness and awareness to reduce social impacts. Earthquakes impacted businesses and dramatically reduced the gross regional product. Seismic Hazard is traditionally assessed using Probabilistic Seismic Hazard Anal- ysis (PSHA). PSHA well represents the hazard at a specific location, but it’s unsatisfactory for spatially distributed systems. Scenario earthquakes overcome the problem representing the actual distribution of shaking over a spatially distributed system. The performance of distributed productive systems during the recovery process needs to be explored. Scenario earthquakes have been used to assess the risk in bridge networks and the social losses in terms of gross regional product reduction. The proposed method for scenario earthquakes has been applied to a real case study: Treviso, a city in the North East of Italy. The proposed method for scenario earthquakes requires three models: one representation of the sources (Italian Seismogenic Zonation 9), one attenuation relationship (Sa- betta and Pugliese 1996) and a model of the occurrence rate of magnitudes (Gutenberg Richter). A methodology has been proposed to reduce thou- sands of scenarios to a subset consistent with the hazard at each location. Earthquake scenarios, along with Mote Carlo method, have been used to simulate business damage. The response of business facilities to earthquake has been obtained from fragility curves for precast industrial building. Fur- thermore, from business damage the reduction of productivity has been simulated using economic data from the National statistical service and a proposed piecewise “loss of functionality model”. To simulate the economic process in the time domain, an innovative businesses recovery function has been proposed. The proposed method has been applied to generate scenarios earthquakes at the location of bridges and business areas. The proposed selection method- ology has been applied to reduce 8000 scenarios to a subset of 60. Subse- quently, these scenario earthquakes have been used to calculate three system performance parameters: the risk in transportation networks, the risk in terms of business damage and the losses of gross regional product. A novel model for business recovery process has been tested. The proposed model has been used to represent the business recovery process and simulate the effects of government aids allocated for reconstruction. The proposed method has efficiently modeled the seismic hazard using scenario earthquakes. The scenario earthquakes presented have been used to assess possible consequences of earthquakes in seismic prone zones and to increase the preparedness. Scenario earthquakes have been used to sim- ulate the effects to economy of the impacted area; a significant Gross Regional Product reduction has been shown, up to 77% with an earthquake with 0.0003 probability of occurrence. The results showed that limited funds available after the disaster can be distributed in a more efficient way

Garigliano nuclear power plant: seismic evaluation of the turbine building

The Italian Garigliano Nuclear Power Plant (NPP) started its energy production in 1963. At present it is in the decommissioning stage. In order to get a proper management of the radioactive waste that will be produced during the dismantling operations it has been considered convenient to convert the turbine building of the plant into a temporary waste repository. This decision posed a remarkable seismic safety assessment issue. As a matter of fact, the challenge was to extend, in satisfactory safety conditions, the use of an important facility that has reached the end of its designed lifetime and to have this extended use approved by nuclear safety agencies. In this context many tasks have been accomplished, of which the most important are: (a) a new appraisal of site seismic hazard; (b) the execution of many investigations and testing on the construction materials; (c) the set up of a detailed 3D finite element model including the explicit representation of foundation piles and soil; (d) consideration of soil structure kinematic and dynamic nteraction effects. This paper describes the adopted seismic safety assessment criteria which are based on a performance objectives design approach. While performance based design is the approach currently recommended by European Regulations to manage seismic risk and it is fully incorporated in the Italian code for conventional buildings, bridges and plants, NPP are not explicitly considered. Therefore it was necessary to delineate a consistent interpretation of prescribed rules in order to properly select the maximum and operating design earthquakes on one side and corresponding acceptable limit states on the other side. The paper further provides an outline of the numerical analyses carried out, of the main results obtained and of the principal retrofitting actions that will be realized

Geodetic Observations of Weak Determinism in Rupture Evolution of Large Earthquakes.

The moment evolution of large earthquakes is a subject of fundamental interest to both basic and applied seismology. Specifically, an open problem is when in the rupture process a large earthquake exhibits features dissimilar from those of a lesser magnitude event. The answer to this question is of importance for rapid, reliable estimation of earthquake magnitude, a major priority of earthquake and tsunami early warning systems. Much effort has been made to test whether earthquakes are deterministic, meaning that observations in the first few seconds of rupture can be used to predict the final rupture extent. However, results have been inconclusive, especially for large earthquakes greater than M w 7. Traditional seismic methods struggle to rapidly distinguish the size of large-magnitude events, in particular near the source, even after rupture completion, making them insufficient to resolve the question of predictive rupture behavior. Displacements derived from Global Navigation Satellite System data can accurately estimate magnitude in real time, even for the largest earthquakes. We employ a combination of seismic and geodetic (Global Navigation Satellite System) data to investigate early rupture metrics, to determine whether observational data support deterministic rupture behavior. We find that while the earliest metrics (~5 s of data) are not enough to infer final earthquake magnitude, accurate estimates are possible within the first tens of seconds, prior to rupture completion, suggesting a weak determinism. We discuss the implications for earthquake source physics and rupture evolution and address recommendations for earthquake and tsunami early warning

Ground-Motion Prediction Models for Arias Intensity and Cumulative Absolute Velocity for Japanese Earthquakes Considering Single-Station Sigma and Within-Event Spatial Correlation

Arias intensity (I-A) and cumulative absolute velocity (CAV) are ground-motion measures that have been found to be well suited to application in a number of problems in earthquake engineering. Both measures reflect multiple characteristics of the ground motion (e.g., amplitude and duration), despite being scalar measures. In this study, new ground-motion prediction models for the average horizontal component of I-A and CAV are developed, using an extended database of strong-motion records from Japan, including the 2011 Tohoku event. The models are valid for magnitude greater than 5.0, rupture distance less than 300 km, and focal depth less than 150 km. The models are novel because they take account of ground-motion data from the 2011 Tohoku earthquake while incorporating other important features such as event type and regional anelastic attenuation. The residuals from the ground-motion modeling are analyzed in detail to gain further insights into the uncertainties related to the developed median prediction equations for I-A and CAV. The site-to-site standard deviations are computed and spatial correlation analysis is carried out for I-A and CAV, considering both within-event residuals and within-event single-site residuals for individual events as well as for the combined dataset

Earthquake Early Warning and Preparatory Phase Detection through the use of Machine Learning Techniques

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

A regional ground motion excitation/attenuation model for the San Francisco region

By using small-to-moderate-sized earthquakes located within ~200 km of San Francisco, we characterize the scaling of the ground motions for frequencies ranging between 0.25 and 20 Hz, obtaining results for geometric spreading, Q(f), and site parameters using the methods of Mayeda et al. (2005) and Malagnini et al. (2004). The results of the analysis show that, throughout the Bay Area, the average regional attenuation of the ground motion can be modeled with a bilinear geometric spreading function with a 30 km crossover distance, coupled to an anelastic function exp(-pi*f*r/V*Q(f)) , where: Q(f)=180f^0.42. A body-wave geometric spreading, g(r)= r^-1.0, is used at short hypocentral distances (r < 30 km), whereas g(r)= r^-0.6 fits the attenuation of the spectral amplitudes at hypocentral distances beyond the crossover. The frequency-dependent site effects at 12 of the Berkeley Digital Seismic Network (BDSN) stations were evaluated in an absolute sense using coda-derived source spectra. Our results show: i) the absolute site response for frequencies ranging between 0.3 Hz and 2.0 Hz correlate with independent estimates of the local magnitude residuals (dML) for each of the stations; ii) moment-magnitudes (MW) derived from our path and site-corrected spectra are in excellent agreement with those independently derived using full-waveform modeling as well as coda-derived source spectra; iii) we use our weak-motion-based relationships to predict motions region wide for the Loma Prieta earthquake, well above the maximum magnitude spanned by our data set, on a completely different set of stations. Results compare well with measurements taken at specific NEHRP site classes; iv) an empirical, magnitude-dependent scaling was necessary for the Brune stress parameter in order to match the large magnitude spectral accelerations and peak ground velocities with our weak-motion-based model