Marzocchi, Warner

A probabilistic seismic hazard model consists of a set of weighted models/branches that describes the center, the body and the range of seismic hazard. Owing to the intrinsic nature of this kind of analysis, the weight of each model/branch represents its scientific credibility. However, practical uses of this model may sometimes require the selection of one or a few hazard curves that are sampled from the whole model, which often consists of thousands of branches. Here we put forward an innovative procedure that facilitates the scoring, ranking and selection of the hazard curves to account for the requirements of a specific application. The approach consists of a careful quality check of the data used for scoring and the adoption of a proper scoring rule. To show the applicability of this approach, we present an example that consists of scoring and ranking a set of multiple models/branches constituting a recent seismic hazard model of Italy. To score these branches, hazard estimates produced by each of them are compared with time series of macroseismic observations available in the Italian macroseismic database for a carefully selected set of localities deemed sufficiently representative, homogeneously distributed in space and complete with respect to time and intensity levels. The proper scoring parameter used for such a comparison is the logarithmic score, which can always be applied independently of the distribution of the data.

In a recent opinion piece Albarello and Paolucci (2023; hereafter, AP23) provide their view as members of the past Seismic Group of the Commissione Grandi Rischi (CGR-SRS) in Italy, which represents the main scientific consultant for Italian Civil Protection, about the difficulty using probabilistic seismic hazard analysis (PSHA) models for building code purposes. Here, we refer to this specific kind of PSHA modeling as National Seismic Hazard Model (NSHM). We agree with AP23 that the topic is of great and general importance, and here we aim at contributing to this discussion by offering our perspective on two points that are at the heart of the matter, concluding that AP23 is misguided in how to deal with them. First, we assert that the credibility of an NSHM has to be rooted only in the use of the best available science, which includes a rigorous testing phase with observations, independent from the consequences in terms of risk. (PSHA deals with what it is.) Second, we claim that the difficulties in accepting a new NSHM with some major changes with respect to the previous model are mostly due to too rigid building code procedures that do not account for the epistemic uncertainty in the hazard estimates.

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.

In this paper, we gather and take stock of the results produced by the Operational Earthquake Forecasting (OEF) system in Italy, during its first 10 yr of operativity. The system is run in real-time: every midnight and after each ML 3.5 + event, it produces the weekly forecast of earthquakes expected by an ensemble model in each cell of a spatial grid covering the entire Italian territory. To e v aluate the performance skill of the OEF-Italy forecasts, we consider here standard tests of the Collaboratory for the Study of Earthquake Predictability, which have been opportunely adapted to the case of the overlapped weekly OEF forecasts; then we also adopt new performance measures borrowed from other research fields, like meteorology, specific to validate alarm-based systems by a binary criterion (forecast: yes/no; occurrence: yes/no). Our final aim is to: (i) investigate possible weaknesses and room for improvements in the OEF-Italy stochastic modelling, (ii) provide performance measures that could be helpful for stakeholders who act through a boolean logic (making an action or not) and (iii) highlight possible features in the Italian tectonic seismic activity.

The protracted nature of the 2016-2017 central Italy seismic sequence, with multiple damaging earthquakes spaced over months, presented serious challenges for the duty seismologists and emergency managers as they assimilated the growing sequence to advise the local population. Uncertainty concerning where and when it was safe to occupy vulnerable structures highlighted the need for timely delivery of scientifically based understanding of the evolving hazard and risk. Seismic hazard assessment during complex sequences depends critically on up-to-date earthquake catalogues-i.e., data on locations, magnitudes, and activity of earthquakes-to characterize the ongoing seismicity and fuel earthquake forecasting models. Here we document six earthquake catalogues of this sequence that were developed using a variety of methods. The catalogues possess different levels of resolution and completeness resulting from progressive enhancements in the data availability, detection sensitivity, and hypocentral location accuracy. The catalogues range from real-time to advanced machine-learning procedures and highlight both the promises as well as the challenges of implementing advanced workflows in an operational environment.

Knowledge of the global rates of volcanism is fundamental for modeling the Earth, as those rates closely relate to plate tectonics, crustal growth, mantle dynamics, atmospheric evolution, climate change, and virtually any aspect of the global Earth dynamics. In spite of their huge relevance, the global rates of volcanism have remained unknown, hidden within data that appeared disordered, largely fragmented and incomplete, reflecting poor preservation of small eruptions in the geological record, rareness of large eruptions, and distributions far from normal. Here we describe and validate a model that reproduces global volcanism to high statistical significance, and that is so simple to comfortably fit on a t-shirt. We use the model to compute the expected rates of global terrestrial volcanism over time windows from 1 to 100,000 years, and validate it by comparing with observations back to a few million years. Notably, the model can be tested against independent observations collected in the near future, a feature which is relatively uncommon among global models of Solid Earth dynamics.

Taroni et al. (2021; hereafter TZM21) proposed a method to perform a spatial b-value mapping based on the weighted-likelihood estimation and applied this method to the Italian region as a tutorial example. In the accompanying comment, Gulia et al. (2021; hereafter GGW21) did not challenge the TZM21’s method, but they argued that the catalog used by TZM21 is contaminated by quarry blasts, introducing a bias that may impact any seismotectonic or hazard interpretations. Although in TZM21 the application to the Italian territory was only a tutorial example and we purposely did not make any thorough discussion on the meaning of the results in terms of seismotectonic or seismic hazards (that would have required many more analyses), we acknowledge the potential role of the quarry blasts, and we add some further analysis here. We thank GGW21 for giving us this opportunity. Here, removing the part of the catalog contaminated by quarry blasts and applying the same analysis as in TZM21, we obtain results that are very similar to the ones reported in TZM21; specifically, only one region that is characterized by low natural seismicity rate shows a marked effect of the quarry blasts on the b-value.

Operational Earthquake Forecasting (OEF) aims to deliver timely and reliable forecasts that may help to mitigate seismic risk during earthquake sequences. In this paper, we build the first OEF system for the State of Israel, and we evaluate its reliability. This first version of the OEF system is composed of one forecasting model, which is based on a stochastic clustering Epidemic Type Earthquake Sequence (ETES) model. For every day of the forecasting time period, January 1, 2016 - November 15, 2020, the OEF-Israel system produces a weekly forecast for target earthquakes with local magnitudes greater than 4.0 and 5.5 in the entire State of Israel. Specifically, it provides space-time-dependent seismic maps of the weekly probabilities, obtained by using a fixed set of the model’s parameters, which are estimated through the maximumlikelihood technique based on a learning period of about 32 years (1983–2015). According to the guidance proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP), we also perform the N- and S-statistical tests to verify the reliability of the forecasts. Results show that the OEF system forecasts a number of events comparable to the observed one, and also captures quite well the spatial distribution of the real catalog with the exception of two target events that occurred in low seismicity regions.

Knowledge of the global distribution of Earth volcanism is critical in many fields of the Geosciences involving large-scale assessments, such as plate tectonics, global volcanic hazards, and climate change. Recent analysis has revealed that global eruption inter-event times are exponentially distributed, implying that on the global scale volcanic eruptions are Poisson distributed. Here, we employ those findings to calibrate a continuous frequency-volume distribution for subaerial eruptions of any size on Earth from small lava flows to super-eruptions. Obtaining such a continuous global distribution implies considering the existing data and the way they are collected and categorized into databases, as well as extending the available eruption volume data to eruption VEI classes less than 4. The continuous global distribution shows an initial log-normal section up to volumes of about 170 Mm3, followed by a power-law section, tapered on its extreme right-end side, encompassing about five orders of magnitude of eruption volumes. The potential implications are discussed in terms of short-term eruption forecasts of the size of an impending eruption, critical for volcanic emergency management.

We describe the main structure and outcomes of the new probabilistic seismic hazard model for Italy, MPS19 [Modello di Pericolosità Sismica, 2019]. Besides to outline the probabilistic framework adopted, the multitude of new data that have been made available after the preparation of the previous MPS04, and the set of earthquake rate and ground motion models used, we give particular emphasis to the main novelties of the modeling and the MPS19 outcomes. Specifically, we (i) introduce a novel approach to estimate and to visualize the epistemic uncertainty over the whole country; (ii) assign weights to each model components (earthquake rate and ground motion models) according to a quantitative testing phase and structured experts’ elicitation sessions; (iii) test (retrospectively) the MPS19 outcomes with the horizontal peak ground acceleration observed in the last decades, and the macroseismic intensities of the last centuries; (iv) introduce a pioneering approach to build MPS19_cluster, which accounts for the effect of earthquakes that have been removed by declustering. Finally, to make the interpretation of MPS19 outcomes easier for a wide range of possible stakeholders, we represent the final result also in terms of probability to exceed 0.15 g in 50 years.