Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

Peresan, Antonella; Gentili, Stefania

doi:10.1016/j.pepi.2017.11.007

Cited by 38 publications

(36 citation statements)

References 49 publications

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“…Fig. 1 shows the region selected for the analysis between latitude 45.5 and 46.75, in good agreement with Peresan and Gentili (2018), extending the longitude to 14.5 after 2008.…”

Section: Catalogue and Selected Areasupporting

confidence: 62%

“…In order to define the region of sufficient completeness of the catalogue, we started from the results of Peresan and Gentili (2018) for the analysis of the catalogue by the nearest neighbor method.…”

Section: Catalogue and Selected Areamentioning

confidence: 99%

“…Due to problems connected with the method used to evaluate coda duration, the use of this magnitude causes some artifacts like e.g. the non-linearity of Gutenberg Richter relation (Gutenberg Richter, 1954) after year 1994 (Peresan and Gentili 2018). The problem is well known (see Bragato and Tento, 2005).…”

Section: Magnitude Corrections and Data Completenessmentioning

confidence: 99%

“…ML = 1.377 * MD-1. 376 (1) Peresan and Gentili (2018) applied eq. 1 for earthquakes after 1994 and show that in this way the Gutenberg Richter relation is linear for ML≥1.…”

Section: Magnitude Corrections and Data Completenessmentioning

confidence: 99%

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Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia

Gentili

Giovambattista

2020

Physics of the Earth and Planetary Interiors

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In this study, we propose an analysis of the earthquake clusters that occurred in North-Eastern Italy and western Slovenia from 1977 to today. Given a mainshock generating alarm in the population, we are interested in forecasting if a similar magnitude earthquake will follow. We classify the earthquake clusters associated with mainshocks of magnitude Mm into two classes: if the strongest aftershock has a magnitude ≥Mm-1 (swarms or large aftershock seismic sequences) as type A, otherwise (smaller aftershocks seismic sequences) as type B. A large aftershock following a main shock can cause significant damages to already weakened buildings and infrastructures, so a timely advisory information to the civil protection is of great interest for effective decision-making. For the first time, we applied to a new catalogue a pattern recognition algorithm for cluster type forecasting that we developed for all Italy (Gentili and Di Giovambattista, 2017). Thanks to the lower completeness magnitude of the local OGS catalogue, compared to the national one, and to a new version of the algorithm, we were able to lower the threshold of the clusters mainshocks magnitude from 4.5 to 3.7. The method has been validated by rigorous statistical tests. We tested the algorithm on the 1976 highly destructive earthquake cluster (mainshock magnitude 6.5the strongest in the last 80 years in the region) and we retrospectively forecasted it as an A cluster.Successful results were obtained also on other three smaller earthquake clusters in 2019. et al., 2016), which caused about 1000 casualties and the complete destruction of little towns and villages, a seismic network managed by OGS (National Institute of Oceanography and Experimental Geophysics) was installed. From 1977, seismicity has been recorded by the OGS network. Main events, with magnitude greater than 5, recorded by the network are: the ML=5.2 16 September 1977 Trasaghis earthquake, in the central-western part of the analysed area, corresponding to Friuli Venezia Giulia Italian region, and two more recent earthquakes respectively on 12 April 1998 (ML=5.6) and on 12 July 2004 (ML=5.1), located near the border between Italy and Slovenia. However, several clusters of lower magnitude have been detected in different parts of the analyzed region. This study is possible thanks to the OGS bulletins, available at the website www.crs.ogs.trieste.it/bollettino/RSFVG/RSFVG.en.html, an accurate local catalogue characterized by low completeness magnitude that has been compiled by the National Institute of Oceanography and Experimental Geophysics, Centre of Seismological Research, since 1977. Evenif many clusters in the region are characterized by a mainshock followed by smaller aftershocks, in some cases repeated strong earthquakes have been recorded. These earthquakes constitute a big danger from the civil protection point of view, because they imply accumulated damage on already damaged structures, increasing the probability of building collapse. The last strong earthquake in the region, the 1976...

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“…Fig. 1 shows the region selected for the analysis between latitude 45.5 and 46.75, in good agreement with Peresan and Gentili (2018), extending the longitude to 14.5 after 2008.…”

Section: Catalogue and Selected Areasupporting

confidence: 62%

Section: Catalogue and Selected Areamentioning

confidence: 99%

Section: Magnitude Corrections and Data Completenessmentioning

confidence: 99%

“…ML = 1.377 * MD-1. 376 (1) Peresan and Gentili (2018) applied eq. 1 for earthquakes after 1994 and show that in this way the Gutenberg Richter relation is linear for ML≥1.…”

Section: Magnitude Corrections and Data Completenessmentioning

confidence: 99%

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Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia

Gentili

Giovambattista

2020

Physics of the Earth and Planetary Interiors

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“…This nearest neighbour method has subsequently been applied or modified to investigate induced seismicity (Schoenball et al 2015;Zaliapin & Ben-Zion 2016b;Schoenball & Ellsworth 2017), the background rate of events (Gentili et al 2017), swarm sequences (Ruhl et al 2016;Zhang & Shearer 2016), Italian earthquake clusters (Peresan & Gentili 2018) and as a comparison cluster identification technique (Moradpour et al 2014;Reverso et al 2015;Maghsoudi et al 2016). However, the binary threshold approach ignores the overlap in the two distributions inherent in a mixture model and therefore ignores uncertainty in the links.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic identification of earthquake clusters using rescaled nearest neighbour distance networks

Bayliss

Naylor

Main

2019

Geophysical Journal International

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S U M M A R YDespite the widely recognized importance of the spatio-temporal clustering of earthquakes, there are few robust methods for identifying clusters of causally related earthquakes. Recently, it has been proposed that earthquakes can be linked to their nearest neighbour events using a rescaled distance that depends on space, time and magnitude. These nearest neighbour links may correspond either to causally related event pairs within a clustered sequence or a non-causal relationship between independent events in different sequences. The frequency distribution of these rescaled nearest neighbour distances is consistent with a two-component mixture model where one component models random background events and the other models causally related clusters of events. To distinguish between these populations, a binary threshold has commonly been used to separate the clustered and background events. This has an obvious weakness in that it ignores the overlap of the two distributions and therefore all uncertainty in the event pair classification. It is also restricted so far to treating the two modes as normal distributions. Here we develop a new probabilistic clustering framework using a Markov Chain Monte Carlo mixture modelling approach which allows overlap and enables us to quantify uncertainty in event linkage. We test three hypotheses for the underlying component distributions. The normal and gamma distributions fail to fit the tails of the observed mixture distribution in a well-behaved way. In contrast, the Weibull mixture model is well-behaved in the tail, and provides a better fit to the data. We demonstrate this using catalogues from Southern California, Japan, Italy and New Zealand. We also demonstrate how this new approach can be used to create probabilistic cluster networks allowing investigation of cluster structure and the spatial, temporal and magnitude distributions of different types of clustering and highlight difficulties in applying simple metrics for cluster discrimination.

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Nowcasting Earthquakes in Southern California with Machine Learning:Bursts, Swarms and Aftershocks May Reveal the Regional Tectonic Stress

Rundle¹,

Donnellan²

2020

Preprint

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Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

Cited by 38 publications

References 49 publications

Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia

Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia

Probabilistic identification of earthquake clusters using rescaled nearest neighbour distance networks

Nowcasting Earthquakes in Southern California with Machine Learning:Bursts, Swarms and Aftershocks May Reveal the Regional Tectonic Stress

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