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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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 (Unpublished) |
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QA Mathematics Mohamed, Nur Anisah Hisam, Mohamad Norikmal Fazli Introduction to worldwide earthquake probability distributions |
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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. |
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Conference or Workshop Item |
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Mohamed, Nur Anisah Hisam, Mohamad Norikmal Fazli |
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Mohamed, Nur Anisah Hisam, Mohamad Norikmal Fazli |
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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 |
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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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