Explaining Foreshock and the Båth Law Using a Generic Earthquake Clustering Model

2021 ◽  
pp. 105-130
Author(s):  
Jiancang Zhuang
Keyword(s):  
Clustering Model ◽  
Earthquake Clustering

The short-term seismicity of the Central Ionian Islands (Greece) studied by means of a clustering model

2019 ◽  
Vol 220 (2) ◽  
pp. 856-875
Author(s):  
Ourania Mangira ◽  
Rodolfo Console ◽  
Eleftheria Papadimitriou ◽  
Maura Murru ◽  
Vasilios Karakostas
Keyword(s):  
Fault System ◽  
Model Parameters ◽  
Short Term ◽  
Aftershock Activity ◽  
Etas Model ◽  
Ionian Islands ◽  
Clustering Model ◽  
Time Space ◽  
Aftershock Sequences ◽  
Earthquake Clustering

SUMMARY Earthquake clustering in the area of Central Ionian Islands (Greece) is statistically modelled by means of the Epidemic Type Aftershock Sequence (ETAS) branching model, which is the most popular among the short-term earthquake clustering models. It is based upon the assumption that an earthquake is not fully related to any other one in particular, but rather to both all previous events, and the background seismicity. The close temporal proximity of the strong ($M \ge 6.0$) events in the study area offers the opportunity to retrospectively test the validity of the ETAS model through the 2014 Kefalonia doublet (Mw 6.1 and Mw 6.0) and the 2015 Lefkada aftershock sequences. The application of a physics-based earthquake simulator to the local fault system produced a simulated catalogue with time, space and magnitude behaviour in line with the observed seismicity. This catalogue is then used for the detection of short-term interactions between both strong and smaller events and the comparison between the two cases. The results show that the suggested clustering model provides reliable forecasts of the aftershock activity. Combining the ETAS model and the simulator code, though, needs to be more deeply examined since the preliminary results show some discrepancy between the estimated model parameters.

A Geotemporal Clustering Model for COVID-19 Projection

2020 ◽  
Author(s):  
N. Bora Keskin ◽  
Xu Min ◽  
Jing-Sheng Jeannette Song
Keyword(s):  
Clustering Model

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

scholarly journals Improving penalized regression-based clustering model in big data

2021 ◽  
Vol 1897 (1) ◽  
pp. 012036
Author(s):  
Sarah Ghanim Mahmood Al-Kababchee ◽  
Omar Saber Qasim ◽  
Zakariya Yahya Algamal
Keyword(s):  
Big Data ◽  
Penalized Regression ◽  
Clustering Model
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