Introduction to worldwide earthquake probability distributions

Modelling the seismicity data is extremely difficult; hence, the assumptions on the distribution of earthquake occurrences play a crucial part in determining seismic hazard. Due to its simplicity and ease of use, the Poisson distribution has been the most common distribution for modelling earthquake...

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Main Authors: Mohamed, Nur Anisah, Hisam, Mohamad Norikmal Fazli
Format: Conference or Workshop Item
Language:English
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Online Access:http://eprints.um.edu.my/35233/1/Dr.%20Nur%20Anisah%20Mohamed%20%40%20A.%20Rahman%209.2022.pdf
http://eprints.um.edu.my/35233/
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spelling my.um.eprints.352332022-10-07T00:50:52Z http://eprints.um.edu.my/35233/ Introduction to worldwide earthquake probability distributions Mohamed, Nur Anisah Hisam, Mohamad Norikmal Fazli QA Mathematics Modelling the seismicity data is extremely difficult; hence, the assumptions on the distribution of earthquake occurrences play a crucial part in determining seismic hazard. Due to its simplicity and ease of use, the Poisson distribution has been the most common distribution for modelling earthquake data over the past year. Nevertheless, the Poisson distribution appears inefficient due to the diversity of earthquake data and the temporal correlations that are common in many real earthquake sequences. The statistical goodness-of-fit tests using worldwide seismicity data from 1921 to 2021 indicate that earthquake temporal occurrences do not always match the commonly used Poisson distribution in earthquake research. On the other hand, the Negative Binomial distribution was discovered to be a better distribution for observed earthquake magnitude distributions, and it may be applied in seismic analysis. Conference or Workshop Item NonPeerReviewed text en http://eprints.um.edu.my/35233/1/Dr.%20Nur%20Anisah%20Mohamed%20%40%20A.%20Rahman%209.2022.pdf Mohamed, Nur Anisah and Hisam, Mohamad Norikmal Fazli Introduction to worldwide earthquake probability distributions. In: Simposium Kebangsangan Sains Matematik (SKSM) Ke-29, 7-8 September 2022, Kuala Lumpur. (Unpublished)
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Mohamed, Nur Anisah
Hisam, Mohamad Norikmal Fazli
Introduction to worldwide earthquake probability distributions
description Modelling the seismicity data is extremely difficult; hence, the assumptions on the distribution of earthquake occurrences play a crucial part in determining seismic hazard. Due to its simplicity and ease of use, the Poisson distribution has been the most common distribution for modelling earthquake data over the past year. Nevertheless, the Poisson distribution appears inefficient due to the diversity of earthquake data and the temporal correlations that are common in many real earthquake sequences. The statistical goodness-of-fit tests using worldwide seismicity data from 1921 to 2021 indicate that earthquake temporal occurrences do not always match the commonly used Poisson distribution in earthquake research. On the other hand, the Negative Binomial distribution was discovered to be a better distribution for observed earthquake magnitude distributions, and it may be applied in seismic analysis.
format Conference or Workshop Item
author Mohamed, Nur Anisah
Hisam, Mohamad Norikmal Fazli
author_facet Mohamed, Nur Anisah
Hisam, Mohamad Norikmal Fazli
author_sort Mohamed, Nur Anisah
title Introduction to worldwide earthquake probability distributions
title_short Introduction to worldwide earthquake probability distributions
title_full Introduction to worldwide earthquake probability distributions
title_fullStr Introduction to worldwide earthquake probability distributions
title_full_unstemmed Introduction to worldwide earthquake probability distributions
title_sort introduction to worldwide earthquake probability distributions
url http://eprints.um.edu.my/35233/1/Dr.%20Nur%20Anisah%20Mohamed%20%40%20A.%20Rahman%209.2022.pdf
http://eprints.um.edu.my/35233/
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