Open AccessAlternative heavy tailed models in seismology
Author(s) -
Miguel Felgueiras,
João Paulo Martins,
Rui Santos
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1564/1/012002
Subject(s) - epicenter , moment (physics) , generalized pareto distribution , seismic moment , pareto principle , pareto distribution , magnitude (astronomy) , energy (signal processing) , scale (ratio) , distribution (mathematics) , seismology , connection (principal bundle) , geology , mathematics , statistics , physics , geography , extreme value theory , fault (geology) , cartography , geometry , mathematical analysis , classical mechanics , astronomy
Great earthquakes are commonly considered as the ones with moment magnitude ( M w ) above or equal to 8.0. Since these earthquakes can destroy entire communities located near the epicentre, the search of physical laws that explain the energy released by them is an important issue. There is a connection between the radiated energy of an earthquake, its magnitude and its seismic moment ( M 0 ). Thence, when fitting a heavy or an extremely heavy tailed distribution to a seismic moment dataset, we are in fact adjusting a mathematical model which explains the amount of energy released by these great seisms. Therefore, the main goal of this work is to study the more appropriated Pareto based models (the most used family in this field) when explaining the seismic moment of the great earthquakes. With this purpose in mind, we selected two different catalogs that accommodate recent events and are considered more accurate than other catalogs used in previous works. We conclude that the traditional Pareto distribution remains a good choice to deal with this kind of data, but Log-Pareto lead to higher p-values and Location-scale Pareto is better fitted to the biggest events.