Selva, Jacopo

Seismic urgent computing enables early assessment of an earthquake’s impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions about rescue and mitigation activities in the affected areas. Uncertainty quantification for urgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present ProbShakemap, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. ProbShakemap incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS ShakeMap software and the OpenQuake-engine. ShakeMap modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the OpenQuake-engine libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. ProbShakemap provides the user with a set of tools to explore, at each POI, the predictive distribution of ground motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between ProbShakemap and ShakeMap output.

Integrating diverse expert opinions in hazard and risk projects is essential to managing subjective decisions and quantifying uncertainty to produce stable and trustworthy results. A structured procedure is necessary to organize the gathering of experts' opinions while ensuring transparency, accountability, and independence in judgements. We propose a novel Multiple-Expert management Protocol (MEP) to address this challenge, providing procedural guidelines for conducting single to multi-hazard risk analyses. MEP establishes a workflow to manage subjectivity rooted in (i) moderated and staged group interactions, (ii) trackable blind advice through written elicitations with mathematical aggregation, (iii) participatory independent review, (iv) close cooperation between scientific and managerial coordination, and (v) proper and comprehensive documentation. Originally developed for stress testing critical infrastructure, MEP is designed as a single, flexible, technology-neutral procedural workflow applicable to various sectors. Moreover, its scalability allows it to adapt from high to low-budget projects and from complex probabilistic multi-hazard risk assessments to standard single-hazard analyses, with different experts' degree and type of involvement depending on available funding and emerging controversies. We present two compelling case studies to showcase MEP's practical applicability: a multi-hazard risk analysis for a port infrastructure and a single-hazard regional tsunami hazard assessment.

PROMETHEUS is a statistical tool that allows creating maps showing the probability of finding tephra deposits of different grain sizes, originating from eruptions of a specific volcanic source, at any location around the vent. It couples wind profiles at different heights in the Mediterranean area with terminal velocity of volcanic particles. The input parameters include the height of the eruption column (which characterizes the intensity of the eruption), wind statistics (directions and intensities), and tephra deposits of a selected grain size. In particular, we used the parameterizations provided by Costa et al. (2016) and performed simulations using the HAZMAP tephra dispersal model to determine the maximum reachable distances that tephra can cover under weak, medium, and strong wind conditions (e.g. 7, 30, and 70 m/s velocities at the tropopause) and with column heights of 10, 20, and 30 km, depositing of at least the loading corresponding to 0.1 mm (corresponding to cryptotephra). Three alternative configurations of the model are validated analyzing first the eruptive source of Somma Vesuvius, with the related explosive eruptions from 22 ka Pomici di Base to the 1944 eruption. A further validation is made by comparing the probabilistic maps with the tephrostratigraphy of known marine and terrestrial cores using standard test of proportions (binomial distributions) and the binary logistic regression model, statistically quantifying the effectiveness of the model against the tephrostratigraphy recorded within this time frame. Based on this validation, a preferred configuration of PROMETHEUS is selected. PROMETHEUS probability maps will guide the selection of sampling sites for specific tephra deposits and could also support the study of the completeness of overall eruption catalogs over time.

A new method for assessing volumes of tephra deposits based on only two thickness data is presented. It is based on the assumptions of elliptical shape for isopachs, a statistical characterization of their eccentricity, and an empirical relationship between their deposit thinning length scale and volumes. The method can be applied if the pair of thickness data are sufficiently distant from the volcano source, with a minimum distance ratio larger than 2. The method was tested against about 40 published volumes, from both equatorial belt and mid-latitude volcanoes. The results are statistically consistent with the published results, demonstrating the usefulness of the method. When applied in forward, the model allowed us to calculate the volume for some important tephra layers in the Mediterranean tephrostratigraphy, providing, for the first time, an assessment of the size of these eruptions or layers.

Nisyros Island (Greece) is affected by widespread gas emissions from fumarolic fields located at the bottom of hydrothermal craters in the southern part of its caldera. This morphology and the current low gas fluxes make Nisyros an ideal site for testing the limits of physics-based gas dispersal models in confined and low-emission conditions. Here, we focused our attention on the local scale volcanic gas dispersion from the Stephanos hydrothermal crater. In April 2023, a 1-week survey was carried out to measure weather data, CO 2 and H 2 S gas fluxes, air concentrations from portable gas stations, and chemical composition of fumarolic gases and to acquire thermal images of the crater floor. These data were used as inputs and boundary conditions for numerical simulations using a DISGAS-2.6.0 model in order to quantify the present-day volcanic degassing and its associated uncertainties, accounting for the meteorological variability. Model results are provided in terms of H 2 S probabilistic exceedance and persistence maps, showing gas concentrations within the crater that fall below the thresholds indicated for the occurrence of serious respiratory problems. Since DISGAS-2.6.0 does not account for chemical reactions, this study represents a good opportunity to discuss the methodological limits of simulating the dispersion of H 2 S which is challenging due to its rapid degradation and dilution in the atmosphere. In this regard, we also provided an empirical law of the H 2 S depletion in low-emission conditions that takes into account the uncertainties related to the field measurements.

Owing to the current lack of plausible and exhaustive physical pre-eruptive models, often volcanologists rely on the observation of monitoring anomalies to track the evolution of volcanic unrest episodes. Taking advantage from the work made in the development of Bayesian Event Trees (BET), here we formalize an entropy-based model to translate the observation of anomalies into probability of a specific volcanic event of interest. The model is quite general and it could be used as a stand-alone eruption forecasting tool or to set up conditional probabilities for methodologies like the BET and of the Bayesian Belief Network (BBN). The proposed model has some important features worth noting: (i) it is rooted in a coherent logic, which gives a physical sense to the heuristic information of volcanologists in terms of entropy; (ii) it is fully transparent and can be established in advance of a crisis, making the results reproducible and revisable, providing a transparent audit trail that reduces the overall degree of subjectivity in communication with civil authorities; (iii) it can be embedded in a unified probabilistic framework, which provides an univocal taxonomy of different kinds of uncertainty affecting the forecast and handles these uncertainties in a formal way. Finally, for the sake of example, we apply the procedure to track the evolution of the 1982–1984 phase of unrest at Campi Flegrei.

The management of multiple hazards simultaneously impacting on a territory is a challenge for effective risk mitigation. This is particularly true on active volcanoes like Mt. Etna, characterized by effusive and explosive eruptions, often coupled with an intense seismic activity. This work aims at presenting the approach of the PANACEA project on the treatment of multi-hazards in terms of risk, which requires a common definition of the exposed elements and their vulnerability. Another aspect emerging from the recent and historical volcanic crises at Etna, is the occurrence of cascading effects and the problem of assessing their short-term interactions. Here we present a risk model taking into account a set of sequences of hazardous events which may result from a volcano unrest to possible impacts to some infrastructural elements. The outcomes of the project are intended to be a significant step towards a more comprehensive resilience to volcanic disasters, leading to a more safe society.

Nowadays, modeling of tephra fallout hazard is coupled with probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position, and meteorological conditions. In this framework, we present a prototypal methodology to carry out the long-term tephra fallout hazard assessment in southern Italy from the active Neapolitan volcanoes: Somma–Vesuvius, Campi Flegrei, and Ischia. The FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high resolution volcanic hazard assessment, relying on a Bayesian procedure, in order to provide the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-year exposure time. Our results are expressed in terms of absolute mean hazard maps considering different levels of aggregation, from the impact of each volcanic source and eruption size class to the quantification of the total hazard. This work provides for the first time, a multi-volcano probabilistic hazard assessment posed by tephra fallout, comparable with those used for seismic phenomena and other natural disasters. This methodology can be applied to any other volcanic areas or over different exposure times, allowing researchers to account for the eruptive history of the target volcanoes that, when available, could include the occurrence of less frequent large eruptions, representing critical elements for risk evaluations.

Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametri zations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those. The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations.

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026).