Description: 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 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.

Description: Induced earthquakes have peculiar characteristics such as relatively shallow depths, small-to-moderate magnitude, correlation with field operations, non-GR recurrence law, and eventually non-homogenous Poisson recurrence time. Thus, when dealing with induced seismicity, the standard Probabilistic Seismic Hazard Analysis (PSHA) has to be modified.This work aims at exploiting the information carried by the ongoing induced seismic sequence in quasi-real time to provide spatio-temporal predictions of ground shaking in a prescribed forecasting interval (in the order of days). First, the workflow adaptively updates 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. The ETAS parameters will be re-calibrated to take into account non-GR long-term temporal boundary conditions in case of induced seismicity.Second, the PSHA is performed using proper ground motion prediction models (GMPE). By combining the time-dependent seismicity rates provided by ETAS model and the mentioned GMPE, PSHA in a prescribed forecasting interval is adopted for calculating the mean rates of exceeding certain ground-shaking levels. The procedure is demonstrated through retrospective hazard forecasting of induced seismicity recorded at the Geysers geothermal field in northern California in the time period of 2011-2015 during fluid injection in the vicinity of Prati 9 and Prati 29 injection wells. This work has been supported by PRIN-2017 MATISSE project No 20177EPPN2, funded by Italian Ministry of Education and Research.

Description: The goal of MATISSE is to develop and implement technologies needed for successfully detecting and quantifying hazards connected with geo-energy operations in the sub-surface, in particular induced seismicity. Anthropogenic earthquakes constitute one of the major environmental impacts associated with geo-resources exploitation. Injection induced events are an undesired dynamic rockmass response to technological processes. Water injection operations taking place during industrial activities such as oil, gas and geothermal exploitation often induce microseismic activity and, under specific circumstances, reactivate existing faults, causing events of considerable size. These industrial activities can also determine air pollution and ground water contamination.Induced seismic hazard is evaluated through the computation and update of ground motion prediction equations, the time-lapse of velocity, attenuation and seismic noise tomography and the computation of pore pressure time variations. All these parameters are correlated with operational activities. A multi-hazard approach aimed at evaluating the adverse effects on environment caused by the sub-surface exploitation of geo-resources is developed.This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by Italian Ministry of Education and Research.

Description: Tsunami hazard and risk analysis are examples of multi-hazard and multi-risk assessments. The procedure for probabilistic tsunami risk analysis (PTRA) involves characterization, quantification, and propagation of uncertainties from sources to the consequences. A forward and modular probabilistic framework for risk assessment, known as the PEER-type approach was originally developed for single-hazard risk assessment. However, given its practical appeal, it has also been adapted to multi-hazard and multi-risk analysis. We focus on the application of this framework for tsunami risk analysis and demonstrate how the uncertainties are going to be propagated from the hazard to the risk level. The advantage of using this approach, compared to fully simulation-based approaches for tsunami risk assessment, is that it can use already-available hazard, fragility, and consequence models. More specifically, the interval of confidence for the hazard curves can be used as a proxy for epistemic uncertainties. The procedure also considers the epistemic uncertainties in the tsunami fragility curves and the consequence functions. We demonstrate how hazard and fragility curves and their confidence intervals, and the loss models (consequence functions) can be integrated to obtain loss curves for certain locations of interest. An application of this procedure is demonstrated for PTRA for Coquimbo Bay in Chile affected by the 2015 Illapel tsunami which was a near-field tsunami generated by a subduction earthquake of 8.3 Mw rupturing a 240 km section of the Nazca–South American plate interface. We consider fragility functions already developed for mixed (masonry and wood) buildings.

Description: A powerful seismic sequence struck the Turkey-Syria border on Feb. 6 with three earthquakes having M〉6.5. At 01:17 UTC, a M7.8 earthquake occurred that was followed 11 minutes later by a M6.7, and around nine hours later by a M7.5. In the first 24 hours, 20 aftershocks with M〉5 and 68 events with M〉4 have been registered. Recently, we have improved and tested a Bayesian simulation-based workflow for spatio-temporal early seismicity forecasting based on ETAS model. It exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information from the ongoing sequence. This workflow is demonstrated and verified through retrospective early seismicity forecasting of Central Italy 2016 and the 2017-2019 western Iran seismic sequences. We test this workflow to predict the spatial distribution of events and their uncertainties for various forecasting intervals within this seismic sequence. Bayesian updating is first employed to learn the ETAS model parameters conditioned on the registered events (that already took place). Then, plausible sequences of events during the forecasting interval are adaptively generated. To this end, we strive to simulate those plausible sequences by embedding a branching process formulation inside the proposed workflow as an alternative to the piece-wise stationary integration of the conditional rate. The latter could be a new feature to the forecasting workflow, while its efficiency in providing early forecasts is explored during this study. This work has been supported by PRIN-2017 MATISSE project No 20177EPPN2, funded by Italian Ministry of Education and Research.

Description: Excellent science is enabled and enriched through making the relevant scientific research tools and results directly accessible to the scientific communities and the civil societies. The European Tsunami Risk Service (ETRiS) aims to collect, harmonize and make available tsunami risk related data (e.g., impact, damage, consequences), data products (e.g., fragility and vulnerability curves), software, and services in a way that they are findable, accessible, interoperable, and reusable by a broad user base. ETRiS is part of the;candidate Thematic Core Service for tsunami (http://tsunamidata.org) and is integrated into the Data Portal of the European Plate Observing System;(EPOS,;https://www.ics-c.epos-eu.org).This web platform (https://eurotsunamirisk.org) provides data products and services required for probabilistic tsunami risk analysis (PTRA) in a multi-risk context. Here are some features:Maps: visualizing data products and data setsData Products: damage scales, fragility curves, consequence models, vulnerability curvesTsunami Impact and Consequence Datasets: selected raw/processed datasets of impact and damage incurred by tsunamiTsunami Risk Modeller’s Toolkit: software and tools for tsunami risk analysis, stand-alone software and tools for post-processing raw data, model testing and validation E-Learning: Online teaching material, Jupiter notebooks, docker applications for probabilistic analysis and uncertainty characterization and propagation, fragility and vulnerability modelling, model testing and selectionUser support and organization of user testing and feedback workshops.We herein present this service and its features and solicit input and collaborations for enriching it and widening its scope.;This work is supported by Horizon Europe Project Geo-INQUIRE. Geo-INQUIRE is funded by the European Commission under project no. 101058518 within the HORIZON-INFRA-2021-SERV-01 call.

Description: 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.

Description: 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.