使用 DBSCAN 演算法對蘇門答臘 BMKG 目錄進行聚類分析;Clustering Analysis of the BMKG Catalog of Sumatra Using the DBSCAN Algorithm

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Title:	使用 DBSCAN 演算法對蘇門答臘 BMKG 目錄進行聚類分析;Clustering Analysis of the BMKG Catalog of Sumatra Using the DBSCAN Algorithm
Authors:	楊念妍;Dwiyanti, Nela Elisa
Contributors:	地球科學學系
Keywords:	One keyword per line;聚類;DBSCAN;地震;蘇門答臘;One keyword per line;Clustering;DBSCAN;Earthquake;Sumatra
Date:	2025-08-26
Issue Date:	2025-10-17 11:48:31 (UTC+8)
Publisher:	國立中央大學
Abstract:	蘇門答臘島位於主要的板塊彙聚邊界，印度-澳洲大洋板塊向東北俯衝至歐亞大陸板塊之下。由於這一區域的斜向俯衝，存在一個重要的海溝平行剪切成分，並由大蘇門答臘斷層 (GSF) 所容納，這是一個主要的右側-沖擊-滑動斷層，沿著蘇門答臘島延伸約 1.900 公里，由 20 段組成。除了這條斷層之外，蘇門答臘島還有另一條斷層，即明打威斷層 (The Mentawai Fault)，這是一條突出的反推構造。此外，此區域會發生外層隆起地震，這是由於板塊在下降到地幔之前的彎曲所造成的伸展應力反應。由於這些構造特徵的交互作用，蘇門答臘被認為是地震活躍且構造複雜的地區。本研究的目的在於識別蘇門答臘的地震群，並探討與地震群相關的潛在構造機制。為了達到此目的，我們運用 Density-Based Spatial Clustering of Applications with Noise (DBSCAN) 演算法來處理 BMKG 橫跨 2009 年至 2022 年的地震目錄。起初，M5 以下地震的規模為 MLv，因此先將其轉換為 Mw。與此同時，規模在 5 級以上的地震已在 Mw 中。本研究使用的完整度為 Mw 4.2。 DBSCAN 根據連線密集元件的概念，將高密度區域識別為叢集，而將低密度區域識別為雜訊或離群。連線密集元件的概念是當點在指定半徑內至少有最小數量的物件時建立的。我們分別檢視半徑和最小點數與各自平方誤差總和的折衷曲線，並在每條曲線的拐角處確定相對應的最佳參數。結果顯示蘇門答臘全島有 24 個不同的地震群，每個地震群在空間上都有很好的分隔。這些地震群根據其基本的構造機制進一步分類，包括與大蘇門答臘斷層 (GSF) 相關的地震群、板塊相關事件、俯衝事件、門塔威斷層以及外層隆起事件。 ;Sumatra Island is located along a major convergent plate boundary, where the Indo-Australian Oceanic Plate is subducting northeastward beneath the Continental Eurasian Plate. Due to the oblique subduction in this area, a significant trench-parallel shear component exists and then accommodated by the Great Sumatran Fault (GSF), a major right-lateral-strike-slip fault that extends approximately 1.900 kilometers along the Sumatra Island and it consists of 20 segments. In addition to this Fault, Sumatra also has another fault, namely The Mentawai Fault, a prominent backthrust structure. Moreover, this region displays outer rise earthquakes as a response of the extensional stress due the bending of the slab prior to its descent into the mantle. Due to all the interaction of these tectonic features, Sumatra is considered as a region that is seismically active and tectonically complex region. The aim of this study is to identify earthquake cluster in Sumatra and investigate the underlying tectonic mechanism associated with clusters. To accomplish this, we apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to the earthquake catalog of BMKG spanning the years 2009 to 2022. Initially, earthquakes with magnitudes below M5 are reported in M_Lv, which are first M_w. In contrast, earthquakes with magnitudes of 5 and above are already in M_w. Magnitude of completeness using in this study is M_w4.2. DBSCAN identifies high-density regions as clusters and low-density regions as noise or outliers based on the concept of connected dense components, established when a point has at least minimum number of objects within a specified radius. We separately examine the trade-off curves of the radius and the minimum number of points against their respective sum of squared errors, and determine the corresponding optimal parameters at the corner of each curve. The results demonstrate 24 distinct earthquake clusters across Sumatra, each spatially well-separated. These clusters are further classified based on their underlying tectonic mechanisms, including those associated with the Great Sumatran Fault (GSF), slab-related events, subduction events, the Mentawai Fault, and outer-rise events.
Appears in Collections:	[Graduate Institute of Geophysics] Electronic Thesis & Dissertation

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