Jacopo Selva

Probability hazard map for future vent opening at the Campi Flegrei caldera, Italy

The Campi Flegrei caldera is a restless structure affected by general subsidence and ongoing resurgence of its central part. The persistent activity of the system and the explosive character of the volcanism lead to a very high volcanic hazard that, combined with intense urbanization, corresponds to a very high volcanic risk. One of the largest sources of uncertainty in volcanic hazard/risk assessment for Campi Flegrei is the spatial location of the future volcanic activity. This paper presents and discusses a long-term probability hazard map for vent opening in case of renewal of volcanism at the Campi Flegrei caldera, which shows the spatial conditional probability for the next vent opening, given that an eruption occurs. The map has been constructed by building a Bayesian inference scheme merging prior information and past data. The method allows both aleatory and epistemic uncertainties to be evaluated. The probability map of vent opening shows that two areas of relatively high probability are present within the active portion of the caldera, with a probability approximately doubled with respect to the rest of the caldera. The map has an immediate use in evaluating the areas of the caldera prone to the highest volcanic hazard. Furthermore, it represents an important ingredient in addressing the more general problem of quantitative volcanic hazards assessment at the Campi Flegrei caldera.

Exploring the evolution of a volcanic seismic swarm: The case of the 2000 Izu Islands swarm

[1] The goal of this paper is to yield physical constrains on the nature and evolution of a seismic swarm by means of a quantitative stochastic modeling of earthquakes occurrence. With this purpose, we fit different stationary and nonstationary stochastic ETAS models to the Izu Islands seismic swarm occurred in 2000. We find that a nonstationary model with background activity and p-value varying through time describes the observations better than other simpler ETAS models. The coherent fluctuations of these parameters and of the spatio-temporal earthquake distribution are interpreted in terms of a magma/fluids source process that evolves through outbursts of activity superimposed to low frequency variations. The results obtained suggest that suitable nonstationary ETAS modeling can be very useful to characterize the nature of the swarm source, and it may provide the basis to build a quantitative tool for tracking in almost real-time the evolution of a magma/fluids source.

Accounting for Epistemic Uncertainty in PSHA: Logic Tree and Ensemble Modeling

Abstract Any trustworthy probabilistic seismic‐hazard analysis (PSHA) has to account for the intrinsic variability of the system (aleatory variability) and the limited knowledge of the system itself (epistemic uncertainty). The most popular framework for this purpose is the logic tree. Notwithstanding its vast popularity, the logic‐tree outcomes are still interpreted in two different and irreconcilable ways. In one case, practitioners claim that the mean hazard of the logic tree is the hazard and the distribution of all outcomes does not have any probabilistic meaning. On the other hand, other practitioners describe the seismic hazard using the distribution of all logic‐tree outcomes. In this article, we explore in detail the reasons for this controversy regarding the interpretation of logic tree, showing that the distribution of all outcomes is more appropriate to provide a joined, full description of aleatory variability and epistemic uncertainty. Then, we provide a more general framework, that we call ensemble modeling, in which the logic‐tree outcomes can be embedded. In this framework, the logic tree is not a classical probability tree, but it is just a technical tool that samples epistemic uncertainty. Ensemble modeling consists of inferring the parent distribution of the epistemic uncertainty from which this sample is drawn. Ensemble modeling offers some remarkable additional features. First, it allows a rigorous and meaningful validation of any PSHA; this is essential if we want to keep PSHA within the scientific domain. Second, it provides a proper and clear description of the aleatory variability and epistemic uncertainty that can help stakeholders appreciate the whole range of uncertainties in PSHA. Third, it may help to reduce the computational time when the logic tree becomes computationally intractable because of too many branches.

Recognizing and tracking volcanic hazards related to non-magmatic unrest: a review

Eruption forecasting is a major goal in volcanology. Logically, but unfortunately, forecasting hazards related to non-magmatic unrest is too often overshadowed by eruption forecasting, although many volcanoes often pass through states of non-eruptive and non-magmatic unrest for various and prolonged periods of time. Volcanic hazards related to non-magmatic unrest can be highly violent and/or destructive (e.g., phreatic eruptions, secondary lahars), can lead into magmatic and eventually eruptive unrest, and can be more difficult to forecast than magmatic unrest, for various reasons. The duration of a state of non-magmatic unrest and the cause, type and locus of hazardous events can be highly variable. Moreover, non-magmatic hazards can be related to factors external to the volcano (e.g., climate, earthquake). So far, monitoring networks are often limited to the usual seismic-ground deformation-gas network, whereas recognizing indicators for non-magmatic unrest requires additional approaches. In this study we summarize non-magmatic unrest processes and potential indicators for related hazards. We propose an event-tree to classify non-magmatic unrest, which aims to cover all major hazardous outcomes. This structure could become useful for future probabilistic non-magmatic hazard assessments, and might reveal clues for future monitoring strategies.

Long-term multi-risk assessment: statistical treatment of interaction among risks

Multi-risk approaches have been recently proposed to assess and compare different risks in the same target area. The key points of multi-risk assessment are the development of homogeneous risk definitions and the treatment of risk interaction. The lack of treatment of interaction may lead to significant biases and thus to erroneous risk hierarchization, which is one of primary output of risk assessments for decision makers. In this paper, a formal statistical model is developed to treat interaction between two different hazardous phenomena in long-term multi-risk assessments, accounting for possible effects of interaction at hazard, vulnerability and exposure levels. The applicability of the methodology is demonstrated through two illustrative examples, dealing with the influence of (1) volcanic ash in seismic risk and (2) local earthquakes in tsunami risk. In these applications, the bias in single-risk estimation induced by the assumption of independence among risks is explicitly assessed. An extensive application of this methodology at regional and sub-regional scale would allow to identify when and where a given interaction has significant effects in long-term risk assessments, and thus, it should be considered in multi-risk analyses and risks hierarchization.

Operational eruption forecasting at high-risk volcanoes: the case of Campi Flegrei, Naples

AbstractHigh risk volcanic events are commonly preceded by long periods of unrest during which scientists are asked to provide near real-time forecasts. The rarity of such events, inaccessibility of the underground volcanic system, non-linear behaviors, and limited datasets constitute major sources of uncertainty. In order to provide reasoned guidance in the face of uncertainties, monitoring observations and conceptual/theoretical models must be incorporated into a formal and structured probabilistic scheme using evidence science principles. As uncertainty and subjectivity are inescapable components of volcanic hazard forecasts, they must be dealt with and clearly communicated to decision-makers and society. Here, we present the set-up of an automated near-real-time tool for short-term eruption forecasting for Campi Flegrei caldera (CFc), Italy. The tool, based on a Bayesian Event Tree scheme, takes account of all the available information, and subjectivity of choices is dealt through a 5-year-long elicitation experiment with a team of about 30 of the major experts of the geological history, dynamics and monitoring of CFc. The tool provides prompt probabilistic assessment in near real-time, making it particularly suitable for tracking a rapidly evolving crisis, and it is easily reviewable once new observations and/or models become available. The quantitative rules behind the tool, which represent the group view of the elicited community of experts, are defined during a period of quiescence, thus allowing prior scrutiny of any scientific input into the model, and minimizing the external stress on scientists during an actual emergency phase. Notably, the results also show that CFc may pose a higher threat to the city of Naples than the better-known Mount Vesuvius.

Systemic seismic risk assessment of road networks considering interactions with the built environment

This article presents an integrated approach for the probabilistic systemic risk analysis of a road network considering spatial seismic hazard with correlation of ground motion intensities, vulnerability of the network components, and the effect of interactions within the network, as well as, between roadway components and built environment to the network functionality. The system performance is evaluated at the system level through a global connectivity performance indicator, which depends on both physical damages to its components and induced functionality losses due to interactions with other systems. An object-oriented modeling paradigm is used, where the complex problem of several interacting systems is decomposed in a number of interacting objects, accounting for intra- and interdependencies between and within systems. Each system is specified with its components, solving algorithms, performance indicators and interactions with other systems. The proposed approach is implemented for the analysis of the road network in the city of Thessaloniki (Greece) to demonstrate its applicability. In particular, the risk for the road network in the area is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake). The potential of road blockages due to collapses of adjacent buildings and overpass bridges is analyzed, trying to individuate possible criticalities related to specific components/subsystems. The application can be extended based on the proposed approach, to account for other interactions such as failure of pipelines beneath the road segments, collapse of adjacent electric poles, or malfunction of lighting and signaling systems due to damage in the electric power network.

Probabilistic short-term volcanic hazard in phases of unrest: A case study for tephra fallout

During volcanic crises, volcanologists estimate the impact of possible imminent eruptions usually through deterministic modeling of the effects of one or a few preestablished scenarios. Despite such an approach may bring an important information to the decision makers, the sole use of deterministic scenarios does not allow scientists to properly take into consideration all uncertainties, and it cannot be used to assess quantitatively the risk because the latter unavoidably requires a probabilistic approach. We present a model based on the concept of Bayesian event tree (hereinafter named BET_VH_ST, standing for Bayesian event tree for short-term volcanic hazard), for short-term near-real-time probabilistic volcanic hazard analysis formulated for any potential hazardous phenomenon accompanying an eruption. The specific goal of BET_VH_ST is to produce a quantitative assessment of the probability of exceedance of any potential level of intensity for a given volcanic hazard due to eruptions within restricted time windows (hours to days) in any area surrounding the volcano, accounting for all natural and epistemic uncertainties. BET_VH_ST properly assesses the conditional probability at each level of the event tree accounting for any relevant information derived from the monitoring system, theoretical models, and the past history of the volcano, propagating any relevant epistemic uncertainty underlying these assessments. As an application example of the model, we apply BET_VH_ST to assess short-term volcanic hazard related to tephra loading during Major Emergency Simulation Exercise, a major exercise at Mount Vesuvius that took place from 19 to 23 October 2006, consisting in a blind simulation of Vesuvius reactivation, from the early warning phase up to the final eruption, including the evacuation of a sample of about 2000 people from the area at risk. The results show that BET_VH_ST is able to produce short-term forecasts of the impact of tephra fall during a rapidly evolving crisis, accurately accounting for and propagating all uncertainties and enabling rational decision making under uncertainty.

Probabilistic tsunami hazard assessment for Messina Strait Area (Sicily, Italy)

The general modular Bayesian procedure is applied to provide a probabilistic tsunami hazard assessment (PTHA) for the Messina Strait Area (MSA), Italy. This is the first study in an Italian area where the potential tsunamigenic events caused by both submarine seismic sources (SSSs) and submarine mass failures (SMFs) are examined in a probabilistic assessment. The SSSs are localized on active faults in MSA as indicated by the instrumental data of the catalogue of the Italian seismicity; the SMFs are spatially identified using their propensity to failure in the Ionian and Tyrrhenian Seas on the basis of mean slope and mean depth, and using marine geology background knowledge. In both cases the associated probability of occurrence is provided. The run-ups were calculated at key sites that are main cities and/or important sites along the Eastern Sicily and the Southern Calabria coasts where tsunami events were recorded in the past. The posterior probability distribution combines the prior probability and the likelihood calculated in the MSA. The prior probability is based on the physical model of the tsunami process, and the likelihood is based on the historical data collected by the historical catalogues, background knowledge, and marine geological information. The posterior SSSs and SMFs tsunami probabilities are comparable and are combined to produce a final probability for a full PTHA in MSA.

Beyond eruptive scenarios: assessing tephra fallout hazard from Neapolitan volcanoes

Assessment of volcanic hazards is necessary for risk mitigation. Typically, hazard assessment is based on one or a few, subjectively chosen representative eruptive scenarios, which use a specific combination of eruptive sizes and intensities to represent a particular size class of eruption. While such eruptive scenarios use a range of representative members to capture a range of eruptive sizes and intensities in order to reflect a wider size class, a scenario approach neglects to account for the intrinsic variability of volcanic eruptions, and implicitly assumes that inter-class size variability (i.e. size difference between different eruptive size classes) dominates over intra-class size variability (i.e. size difference within an eruptive size class), the latter of which is treated as negligible. So far, no quantitative study has been undertaken to verify such an assumption. Here, we adopt a novel Probabilistic Volcanic Hazard Analysis (PVHA) strategy, which accounts for intrinsic eruptive variabilities, to quantify the tephra fallout hazard in the Campania area. We compare the results of the new probabilistic approach with the classical scenario approach. The results allow for determining whether a simplified scenario approach can be considered valid, and for quantifying the bias which arises when full variability is not accounted for.