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  • 标题:CLUSTER ANALYSIS AND SEISMICITY PARTIONING FOR NORTHERN SUMATERA USING MACHINE LEARNING APPROACH
  • 本地全文:下载
  • 作者:EVA DARNILA ; KERISTA TARIGAN ; SUNARDI
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2021
  • 卷号:99
  • 期号:2
  • 页码:370
  • 出版社:Journal of Theoretical and Applied
  • 摘要:Tectonic activity in the past one year in North Sumatra is quite active as it�s located in seismic region on the fault zone accommodates most of the strike-slip movements associated with the sloping convergence between the Indo-Australian and Eurasian plates. In this case, we used clustering approach to see the potential for earthquakes originating from fault activities in Northern Sumatra recorded by seismic network sensors that have been installed either Broadband type or the new mini region. Northern Sumatra based on the source of earthquake activity can be divided into several segments. The main goal of this study to identify the distribution of the Northern Sumatra inland earthquake based on segment activity using the clustering approach. The result shown that the dominance of onshore earthquakes in North Sumatra varies greatly based on magnitude. The magnitude frequency distribution for the period 2019 and January to June 2020 was dominated by earthquakes with magnitudes below 4. The clustering approach in this study illustrates the classification of magnitude of terrestrial earthquakes in North Sumatra in the categories of minor, light and moderate. In 2019 and January-June 2020 period the dominance of the most sources of earthquake was due to the Aceh Central segment.
  • 关键词:Cluster; Machine Learning Approach; Earthquakes
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