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The present work proposes a simulation-based Bayesian method for parameter estimation and fragility model selection for mutually exclusive and collectively exhaustive (MECE) damage states. This method uses an adaptive Markov chain Monte Carlo simulation (MCMC) based on likelihood estimation using point-wise intensity values. It identifies the simplest model that fits the data best, among the set of viable fragility models considered. The proposed methodology is demonstrated for empirical fragility assessments for two different tsunami events and different classes of buildings with varying numbers of observed damage and flow depth data pairs. As case studies, observed pairs of data for flow depth and the corresponding damage level from the South Pacific tsunami on 29 September 2009 and the Sulawesi–Palu tsunami on 28 September 2018 are used. Damage data related to a total of five different building classes are analysed. It is shown that the proposed methodology is stable and efficient for data sets with a very low number of damage versus intensity data pairs and cases in which observed data are missing for some of the damage levels.

SUMMARY The reliable forecasting of seismic sequences following a main shock has practical implications because effective post-event response is crucial in earthquake-stricken regions, aftershocks can progressively cause increased damage and compound economic losses. In the South Iceland Seismic Zone (SISZ), one of two large transform zones in Iceland where earthquake hazard is the highest, an intense seismic sequence took place during 17–24 June 2000, starting with a ${M}_{\rm{w}}$ 6.4 main shock on 17 June 2000, followed by another ${M}_{\rm{w}}$ 6.5 main shock four days later and on a different fault. Both earthquakes caused considerable damage and incurred heavy economic losses. They were immediately followed by intense aftershock activity on the causative faults and triggered earthquakes as far as 80 km away along the transform zone. To investigate the feasibility of forecasting the progression of such complex sequences, we calibrated a spatio-temporal epidemic-type aftershock sequence (ETAS) clustering model to the June 2000 seismic sequence in the framework of Bayesian statistics. Short-term seismicity forecasts were carried out for various forecasting intervals and compared with the observations, the first generated a few hours after the first main shock and followed by daily forecasts. The reliability of the early forecasts was seen to depend on the initial model parameters. By using an adaptive parameter inference approach where the posteriors from each preceding forecasting interval served as informative priors for the next, the fast convergence of the parametric values was ensured. As a result, the 16–84 percentile range of the forecasted number of events captured the actual number of observed events in all daily forecasts, and the model exhibited a strong spatial forecasting ability, even only a few hours after the main shock, and over all subsequent daily forecasts. We present the spatio-temporal ETAS parameters for the June 2000 sequence as ideal candidates of prior estimates for future operational earthquake forecasting of other Icelandic aftershock sequences. Past seismic sequences need to be analysed retrospectively to confirm the stability of the parameters of this study, effectively enable the application of the Bayesian ETAS model as an operational earthquake forecasting system for aftershocks in Iceland.

Forecasting the spatio-temporal occurrence of events is at the core of Operational Earthquake Forecasting, which is of great interest for risk management, particularly during ongoing seismic sequences. Epidemic type aftershock sequence (ETAS) models are powerful tools to estimate the occurrence of events during earthquake sequences. In this context, a robust seismicity forecasting framework based on Bayesian-inference has been adapted to the Patras and Aegio region in western Greece (one of the most seismically active parts of Mediterranean), and an incremental adaptive algorithm is introduced to train the priors for ETAS model parameters. The seismicity forecasting is capable of accounting for uncertainty in the model parameters as well as variations in the sequence of events that may happen during the forecasting interval. Six seismic sequences between 1995 and 2018 were selected with main shock moment magnitudesMw≥6.0. The ETAS model was adapted for each seismic sequence. The number of forecasted events withMw≥4.5 and their spatial distribution was retrospectively compared with the as-recorded earthquake catalogue, confirming a good agreement between the forecasts and observations. The results show that the adapted model can be used immediately after a severe main shock to statistically predict potentially damaging earthquakes during the ongoing seismic sequence. The seismicity forecasts were translated to short-term daily exceedance rates for different thresholds of peak ground acceleration. The results reveal that the seismic hazard increased by up to 33 times in the case of the damaging 1995Mw6.5 earthquake in the city of Aegio. However, the results confirmed that in all six studied sequences, the increased seismic hazard decayed rapidly during the 2 d after the main shock, and remained relatively high in the following days (roughly ten times the long-term time-independent hazard).

Special issue EPOS a Research Infrastructure in solid Earth: open science and innovation .-- 21 pages, 8 figures, Tsunamis constitute a significant hazard for European coastal populations, and the impact of tsunami events worldwide can extend well beyond the coastal regions directly affected. Understanding the complex mechanisms of tsunami generation, propagation, and inundation, as well as managing the tsunami risk, requires multidisciplinary research and infrastructures that cross national boundaries. Recent decades have seen both great advances in tsunami science and consolidation of the European tsunami research community. A recurring theme has been the need for a sustainable platform for coordinated tsunami community activities and a hub for tsunami services. Following about three years of preparation, in July 2021, the European tsunami community attained the status of Candidate Thematic Core Service (cTCS) within the European Plate Observing System (EPOS) Research Infrastructure. Within a transition period of three years, the Tsunami candidate TCS is anticipated to develop into a fully operational EPOS TCS. We here outline the path taken to reach this point, and the envisaged form of the future EPOS TCS Tsunami. Our cTCS is planned to be organised within four thematic pillars: (1) Support to Tsunami Service Providers, (2) Tsunami Data, (3) Numerical Models, and (4) Hazard and Risk Products. We outline how identified needs in tsunami science and tsunami risk mitigation will be addressed within this structure and how participation within EPOS will become an integration point for community development, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive kernel Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017-2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively.

The present work proposes a simulation-based Bayesian method for parameter estimation and fragility model selection for mutually exclusive and collectively exhaustive (MECE) damage states. This method uses an adaptive Markov chain Monte Carlo simulation (MCMC) based on likelihood estimation using point-wise intensity values. It identifies the simplest model that fits the data best, among the set of viable fragility models considered. The proposed methodology is demonstrated for empirical fragility assessments for two different tsunami events and different classes of buildings with varying numbers of observed damage and flow depth data pairs. As case studies, observed pairs of data for flow depth and the corresponding damage level from the South Pacific tsunami on 29 September 2009 and the Sulawesi–Palu tsunami on 28 September 2018 are used. Damage data related to a total of five different building classes are analysed. It is shown that the proposed methodology is stable and efficient for data sets with a very low number of damage versus intensity data pairs and cases in which observed data are missing for some of the damage levels.

The injection of fluids into the subsoil, which takes place during the exploitation of geothermal resources, may affect the quality of the air and the water in the subsoil, and it may be the source of induced earthquakes. A decision support system based on multi-hazard risk (seismic and environmental) analysis can help control the exploitation and keep risk in tolerable levels. In this work, we describe a traffic light system of this type applied in the case of the Basel geothermal site. This system relies on a Bayesian network to compute the probability of the red-amber-green lights for a given distribution of the rate of injection of fluids. The network has a single parent node for the injection rate. Here, we used data on European Plate Observing System Thematic Core Service for Anthropogenic Hazards for the injection rate, the air quality, and the water quality to characterize the links between the parent node and the nodes for air and water quality. The link between the injection rate and the node for the ground motion intensity measure was characterized based on an event-based probabilistic seismic hazard analysis using stochastic earthquake catalogs generated by a physics-based model. Subsequently, arbitrary thresholds of the peak ground velocity, steam production rate, and proppant injection rate, were selected for the amber and red lights to characterize the links between the corresponding nodes and their descendant node, i.e., the traffic light. The probabilities of the traffic lights were computed, and the probability of the red light was disaggregated to identify its driver in different scenarios.

Methodology:A fragility model expresses the probability of exceeding certain damage levels for a given level of intensity for a specific class of buildings or infrastructure. An empirical tsunami fragility curve for a given damage level is derived based on observed pairs of data for the tsunami intensity measure and the corresponding damage level. Tsunami inundation depth and/or flow velocity are usually adopted as scalar intensity measures (they can also be employed together as a vector-valued intensity measure). Physical damage levels are usually defined in a hierarchical manner, implying discrete, mutually exclusive, and collectively exhaustive (MECE) damage states. This means that the fragility curves for consecutive hierarchical damage levels must not intersect. It is clear that by fitting empirical fragility curves to each single damage level, this condition is not automatically satisfied. To overcome this problem, ordered (“parallel”) fragility models or partially ordered models have been adopted in the literature to derive fragility curves for MECE damage states. Empirical tsunami fragility curves are usually constructed using generalized linear regression models by adopting probit, logit, or the complementary loglog link functions. As far as model comparison and selection are concerned, established statistical approaches have been used in recent literature to identify the optimal link function among those mentioned above. Moreover, for estimating the uncertainty in the resulting empirical fragility curves, bootstrap resampling has been commonly used.The present work proposes a simulation-based Bayesian method for inference and model class selection to perform ensemble modelling of the tsunami fragility curves for MECE damage states and the related uncertainties for a given class of buildings. The method uses adaptive Markov Chain Monte Carlo Simulation (MCMC), based on likelihood estimation using point-wise intensity values, to estimate the fragility model parameters and the uncertainties. Among the set of viable fragility models considered, Bayesian model class selection is used to identify the simplest model that fits the data best (i.e., is a parsimonious model). The proposed method provides consistent parameter estimation and confidence intervals for MECE the damage states and identifies the best fragility model class among the pool of viable models, based on a single set of simulation realizations. The whole procedure is provided as open-source software on the site of the European Tsunami Risk Service (https://eurotsunamirisk.org/software/) and is also available as a standalone docker application.Application:As the case-study application, the central South Pacific region-wide tsunami on September 29, 2009 is used. The tsunami was triggered by an unprecedented earthquake doublet (Mw 8.1 and Mw 8.0). The tsunami seriously impacted numerous locations in the central South Pacific. Herein, the damage data related to 120 brick masonry residential buildings associated with the reconnaissance survey sites of American Samoa and Samoa islands were utilized as a proof of concept. A six-tier damage scale is considered, and tsunami inundation depth has been used as the intensity measure. Keywords: probabilistic tsunami risk analysis, tsunami fragility, Bayesian inference, model class selection

Methodology:Induced earthquakes have peculiar characteristics such as, relatively shallow depths, small magnitude, correlation with field operations, non-GR recurrence law, and eventually non-homogenous Poisson recurrence time. Indeed, induced seismicity tends to cluster in limited volumes near the wells where field operations (e.g., fluids injection, extraction, fracking, etc.) are performed. A novel and fully-probabilistic simulation-based procedure is presented for providing temporal and volumetric predictions of induced events’ occurrence in a prescribed forecasting time interval (in the order of hours or days). The procedure aims at exploiting the information provided by the ongoing sequence in quasi-real time (even in the presence of very limited registered data) to adaptively update the seismicity forecasts based on the incoming information as it becomes available. The clustering of seismic events in volume (3D seismicity) and time is modelled based on an Epidemic Type Aftershock Sequence (ETAS) model. The proposed 3D ETAS model encompasses a decoupled depth-area volumetric probabilistic kernel that incorporates kernel density functions for areal extent as well as the focal depth. The ETAS parameters are going to be re-calibrated in order to take into account non-GR long-term temporal boundary conditions in case of induced seismicity. Moreover, exact spatial integrals will be used to consider the 3D boundary conditions. The proposed procedure considers the uncertainties in the earthquake occurrence model parameters in a Bayesian updating framework. Pairing up the Bayesian inference and the suitable efficient simulation schemes (using Markov Chain Monte Carlo Simulation) provides the possibility of performing the forecasting procedure with minimum (or no) need of human interference.Application:The procedure is demonstrated through retrospective forecasting of induced seismicity recorded at the Geysers geothermal field in northern California in the time period of 2011-2015. Injection of cold water and heavier liquids in the hot reservoir caused induced earthquakes with moment magnitudes in the range of [0.0, 4.0] and depth ranging up to 5 km. The proposed procedure is examined for both Bayesian updating of the proposed 3D ETAS model parameters and forecasting of the number of events of interest expected to occur in various time intervals before and after a number of main events within the seismic sequence. The seismicity is predicted within a confidence interval from the mean estimate. Adding a kernel density for the focal depth and moving towards the 3D seismicity forecasting leads to the forecasted number of events that better match the events that actually took place in the forecasting interval, as compared to the 2D ETAS model. Therefore, it is concluded that the proposed 3D ETAS model is quite effective in case of induced seismicity.

Probabilistic tsunami hazard and risk analysis (PTHA/PTRA) is an emerging scientific discipline within the tsuanmi community and allows potentially to incorporate the diverse sources of uncertainty into disaster prevention, preparedness, and mitigation activities. While there are a number of successful applications of this paradigm, it is still an emerging field with a number of unresolved research questions. In a collaborative effort members of the COST Action AGITHAR assessed the existing research gaps for PTHA/PTRA and identified almost 50 different topics worth of further research. An ad hoc expert judgement was conducted to weight these open questions with respect to their expected impact on the quality of the PTHA/PTRA results and their difficulty to be answered. The results of this collaborative effort will be reported highlighting the most challenging and most severe research gaps.The presentation is based on the following publication:J. Behrens, F. Løvholt, F. Jalayer, et al. (2021): Probabilistic Tsunami Hazard and Risk Analysis – A Review of Research Gaps, Frontiers in Earth Science, 9:114, DOI:10.3389/feart.2021.628772.

Supplementary data: The following is the Supplementary data to this article: Acrobat PDF file (2MB) available at: https://ars.els-cdn.com/content/image/1-s2.0-S2212420921007329-mmc1.pdf Copyright © 2022 The Authors. Very large tsunamis are associated with low probabilities of occurrence. In many parts of the world, these events have usually occurred in a distant time in the past. As a result, there is low risk perception and a lack of collective memories, making tsunami risk communication both challenging and complex. Furthermore, immense challenges lie ahead as population and risk exposure continue to increase in coastal areas. Through the last decades, tsunamis have caught coastal populations off-guard, providing evidence of lack of preparedness. Recent tsunamis, such as the Indian Ocean Tsunami in 2004, 2011 Tohoku and 2018 Palu, have shaped the way tsunami risk is perceived and acted upon. Based on lessons learned from a selection of past tsunami events, this paper aims to review the existing body of knowledge and the current challenges in tsunami risk communication, and to identify the gaps in the tsunami risk management methodologies. The important lessons provided by the past events call for strengthening community resilience and improvement in risk-informed actions and policy measures. This paper shows that research efforts related to tsunami risk communication remain fragmented. The analysis of tsunami risk together with a thorough understanding of risk communication gaps and challenges is indispensable towards developing and deploying comprehensive disaster risk reduction measures. Moving from a broad and interdisciplinary perspective, the paper suggests that probabilistic hazard and risk assessments could potentially contribute towards better science communication and improved planning and implementation of risk mitigation measures. COST (European Cooperation in Science and Technology); Royal Society, UK (grant number CHL\R1\180173); Severo Ochoa Centers of Excellence Program (CEX 2018-000797-S) funded by MCIN/ AEI /10.13039/501100011033; Lloyd's Tercentenary Research Foundation, the Lighthill Risk Network, and the Lloyd's Register Foundation-Data Centric Engineering Programme of the Alan Turing Institute.