Frequency-magnitude Distribution and Fractal Dimension of the Seismicity along the Sumatra-Andaman Subduction Zone: A Temporal Comparison
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Creator Toatin Ketthong
Title Frequency-magnitude Distribution and Fractal Dimension of the Seismicity along the Sumatra-Andaman Subduction Zone: A Temporal Comparison
Contributor Santi Pailoplee
Publisher Department of Geology, Chulalongkorn University
Publication Year 2560
Journal Title Bulletin of Earth Sciences of Thailand (BEST)
Journal Vol. 9
Journal No. 1
Page no. 23-Dec
Keyword Earthquake Catalogue, Earthquake Declustering, b value, Fractal Dimension, Sumatra-Andaman Subduction Zone.
URL Website https://www.bestjournal.org/
Website title Bulletin of Earth Sciences of Thailand
ISSN 1906-280X
Abstract The Sumatra-Andaman subduction zone (SASZ) is formed by the Indo-Australian and Eurasian plates collision. Among the tectonic subduction zone in the world, the SASZ is one of the active subduction zones which has generated continuously many great earthquakes. The main aim of this research is to investigate temporally the seismicity by two statistical-seismological methods, i.e., fractal dimension (Dc value) and frequency-magnitude distribution (b value). In addition, this research focused on two groups of seismicity data which are i) before and ii) after earthquake declustering process. In individual dataset, both Dc and b values were investigated temporally at the epicenters of 6 earthquakes with magnitude ? 8.0. The results according to both after-declustering and before-declustering dataset revealed that b value was low before the earthquake occurred in all 6 earthquake case studies approximately 0.91-0.36 and 0.79-0.56. For Dc value of both after and before declustering datasets are nearly reached 2.0. It means that the pattern of earthquake occrences distribute in the areal or plane source. In addition according to temporal investigation, all the B-Dc relationship of 6 case studies along the SASZ is defined as negative relationship. Finally, the results analyzed from after- and before-declustering dataset were similar which meaningful for B-Dc investigation.
Chulalongkorn University

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