Exploring the feasibility of a nationwide earthquake early warning system in Italy

Picozzi, Matteo; Zollo, Aldo; Brondi, Piero; Colombelli, Simona; Elia, Luca; Martino, Claudio

doi:10.1002/2014jb011669

Cited by 49 publications

(43 citation statements)

References 30 publications

(66 reference statements)

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“…Despite the analysis being carried out offline, given the experience gained from the use of the software PRESTo (among the others, Satriano et al [2011] and Picozzi et al [2015]), from which most of the algorithms used in our procedure are taken, and a seismic network with real-time data telemetry capability, we anticipate that the processing time necessary for the analysis of rockslide signals (i.e., the time necessary for the end-of-event declaration to the required M D computation) is in the range of few tens of seconds. Our method has been tested on an independent data set of raw seismic signals generated by a recent rockslide event occurred in the Antelao massif, eastern Italian Alps.…”

Section: Discussionmentioning

confidence: 99%

“…Further investigation on the effects of network density and on the resolution of our algorithm will be performed (e.g., similar to what has been done by Picozzi et al [2015] to assess earthquake early warning feasibility in Italy), including testing activities for hypothesizing the implementation of a landslide detection and rapid response system at the regional/national scale. Preliminary results confirm the effectiveness of our integrated approach on different case studies located in the European Alps and in the Italian Apennines.…”

Section: Discussionmentioning

confidence: 99%

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Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

Manconi

Picozzi

Coviello

et al. 2016

Geophysical Research Letters

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We propose a new real‐time approach to detect, locate, and estimate the volume of rockslides by analyzing waveforms acquired from broadband regional seismic networks. The identification of signals generated by rockslides from other sources, such as natural and/or induced earthquakes, is accomplished by exploiting the ratio between local magnitudes (ML) and duration magnitudes (MD). We found that signals associated with rockslides have ML/MD < 0.8, while for earthquakes ML/MD ≅ 1. In addition, we derived an empirical relationship between MD and rockslide volumes, obtaining a preliminary characterization of rockslide volume within seconds after their occurrence. The key points of this study are presented by testing the hypothesis on a recent rockslide event that occurred in northern Italy. We discuss also the potential evolution of the methodology for early warning and/or rapid response purposes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

Manconi

Picozzi

Coviello

et al. 2016

Geophysical Research Letters

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“…For this reason, attempts have been made in both the literature and common practices to develop decision support systems [27,58,44,37] that would be able to support decision-making. For example, Picozzi et al [42] devised an early warning tool for earthquakes which provides alert messages within about 5-10 s for seismic hazard zones, while Alfieri et al [2] analyzed a European operational warning tool for water-related disasters. Another line of works are focused on developing decision analytical models that provide a better understanding of how to use required variables, and thus improve decisionmaking [40,15,12].…”

Section: Decision-making Process In Control Roomsmentioning

confidence: 99%

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

Horita

Albuquerque

Marchezini

2018

International Journal of Disaster Risk Reduction

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warwick.ac.uk/lib-publications A B S T R A C TThe tasks of disaster risk monitoring and early warning are an important means of improving the efficiency of disaster response and preparedness. However, although the current works in this area have sought to provide a more accurate and better technological infrastructure of systems to support these tasks, they have failed to examine key features that may affect the decision-making. In light of this, this paper aims to provide an understanding of the decision-making process in control rooms for disaster risk monitoring and early warning. This understanding is underpinned by a conceptual framework, which has been developed in this work and describes factors that influence the decision-making. For doing so, data were collected through a series of semi-structured interviews and participatory observations and later evaluated with members of the control room of the Brazilian Center for Monitoring and Early Warning of Natural Disasters (Cemaden). The study findings provided a solid basis for designing the conceptual framework of the essential factors required by the decision-makers. These factors are separated into two groups: 1) the "dimensions" of decision-making (i.e., the type of hazard, the phase of the disaster risk, the location, and area of expertise of the operators) and the "pillars" of decision-making (i.e., the tasks, their required information, useful data sources, and the decision rule). Finally, the contributions achieved in this study may help operators to understand and propose proactive measures that could improve their decision-making, overcome uncertainties, standardize the team's decision-making, and put less pressure on operators.

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“…This shows that, in most cases, sites with intensities greater than or equal to VI would only get a timely warning in the case of an M >6:5 event. Using a similar system in Italy, Picozzi et al (2015) conclude that positive warning times at sites with intensity ground motions larger than VII on the European Macroseismic Scale are only possible for events with M w > 6:5.…”

Section: Introductionmentioning

confidence: 97%

The Gutenberg Algorithm: Evolutionary Bayesian Magnitude Estimates for Earthquake Early Warning with a Filter Bank

Meier¹,

Heaton²,

Clinton³

2015

Bulletin of the Seismological Society of America

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Earthquake early warning (EEW) is a race against time. In particular, at proximal sites to the epicenter (typically the most heavily affected sites), strong ground motion starts shortly after the P-wave onset. For these sites, regional-type EEW systems that wait until data from several stations are available before issuing a warning and that require fixed data windows following a trigger are not fast enough. Single-station algorithms, on the other hand, have high uncertainties that compromise their usefulness. In this article, we propose that uncertainties of the earliest warning messages can be reduced substantially if the broadband frequency information of seismic signals is fully exploited. We present a novel probabilistic algorithm for estimating EEW magnitudes. The Gutenberg algorithm uses a filter bank for a time-frequency analysis of the realtime signals and estimates the posterior probabilities of both magnitude and sourcestation distance directly from the observed frequency content. It starts off as a singlestation algorithm and then naturally evolves into a regional-type algorithm, as more data become available. Using an extensive near-source waveform data set, we demonstrate that the Gutenberg parameter estimates reach the estimation accuracy and precision of existing regional-type EEW systems with only 3 s of data from a single station. The magnitude estimates, however, saturate at a threshold magnitude that depends on the available signal length that is used for the estimation, suggesting that current EEW magnitude estimates (1) are observational rather than predictive and (2) have to be considered minimum estimates, depending on the amount of available data.

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Exploring the feasibility of a nationwide earthquake early warning system in Italy

Cited by 49 publications

References 30 publications

Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

Real‐time detection, location, and characterization of rockslides using broadband regional seismic networks

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

The Gutenberg Algorithm: Evolutionary Bayesian Magnitude Estimates for Earthquake Early Warning with a Filter Bank

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