Spatio-Temporal Modeling of Seismic Provinces of Iran Using DBSCAN Algorithm

Beydokhti, Mohammad Kazemi; Abbaspour, Rahim Ali; Mojarab, Masoud

doi:10.1007/s00024-017-1507-0

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Hasil cluster menunjukkan bahwa kualitas cluster yang diperoleh mewakili berbagai zona seismik sesuai dengan peta seismik India Bureau of India Standards IS 1892:2022. Algoritma DBSCAN banyak digunakan untuk clustering gempabumi secara spasial seperti yang dilakukan oleh (Kazemi-Beydokhti et al, 2017) pada penelitiannya yaitu, melakukan pengelompokkan gempabumi di Iran menggunakan algoritma DBSCAN dengan tujuan untuk mengembangkan model kegempaan secara spatio-temporal di Iran berdasarkan kemiripan perilaku seismik dalam bentuk cluster terpisah. Hasil clustering menunjukkan kemiripan dengan penelitian sebelumnya yang dilakukan oleh (Mojarab et al, 2014) dan (Ansari et al, 2009) pada daerah yang disajikan di dalam model.…”

Section: Penelitian Terkaitunclassified

“…Metode ini pertama kali dikenalkan oleh Ester et al, 1996 yang menerapkan nilai minimum parameter input dan untuk mengelompokkan database spasial yang besar (Daszykowski & Walczak, 2009). Metode ini merupakan metode pengelompokan berdasarkan kepadatan dan cocok diterapkan pada data yang besar (Kazemi-Beydokhti et al, 2017). Algortima DBSCAN dapat digunakan untuk mendeteksi data outlier atau noise dan tidak perlu menentukan jumlah cluster terlebih dahulu (Algorithm, 2021).…”

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Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN

Arafat,

Hariyadi,

Santoso

et al. 2023

JTIIK

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Wilayah Regional VII meliputi Jawa Tengah, Yogyakarta, dan Jawa Timur merupakan wilayah tektonik yang aktif karena terletak di wilayah zona subduksi lempeng Indo-Australia dan Eurasia serta terdapat beberapa patahan aktif di daratan. Oleh karena itu, perlu dilakukan klasifikasi gempabumi untuk memetakan zona rawan gempabumi berdasarkan sumbernya di wilayah Regional VII berdasarkan kesamaan atribut salah satunya adalah berdasarkan karakteristik gempabumi dari sumber yang sama. Pada penelitian ini digunakan pendekatan algoritma Unsupervised Learning Clustering berbasis kepadatan yaitu, Density Based Spatial Clustering of Application with Noise atau DBSCAN, algoritma ini membutuhkan parameter input epsilon (ε) dan MinPts. Data yang digunakan pada penelitian ini adalah data gempabumi wilayah Regional VII tahun 2017 hingga 2021 yang diperoleh dari BMKG. Selanjutnya, proses clustering dilakukan dengan membagi data gempabumi berdasarkan periode yaitu periode tahunan dan periode lima tahun dengan tujuan untuk mengetahui pola cluster berdasarkan periode waktu. Hasil yang terbentuk selanjutnya dievaluasi menggunakan Silhouette Coefficient serta dibandingkan dengan peta Seismisitas Jawa yang telah ada dari katalog PuSGeN 2017. Hasil clustering menggunakan DBSCAN diperoleh jumlah cluster sebanyak 2 hingga 6 cluster dengan nilai Silhouette Coefficient terendah sebesar 0.270 untuk periode T5_2017-2021 dan tertinggi sebesar 0.499 untuk periode T1_2020. AbstractRegional VII area covering Central Java, Yogyakarta and East Java is an active tectonic region because it is located in the subduction zone of the Indo-Australian and Eurasian plates and there are several active faults on land. Therefore, it is necessary to classify earthquakes to map earthquake-prone zones based on their sources in Regional VII area based on the similarity of attibutes, based on the characteristics of earthquakes from the same source. In this study, a density-based Unsupervised Learning Clustering algorithm approach was used namely, Density Based Spatial Clustering of Application with Noise or DBSCAN, this algorithm requires the input parameters epsilon (ε) and MinPts. The data used in this study are earthquake data for Regional VII from 2017 to 2021 obtained from the BMKG. Then, the clustering process is carried out by dividing earthquake data based on the period, namely the annual period and the five-year period with the aim of knowing the pattern of cluster based on the time period. The results are then evaluated using the Sillhouette Coefficient and compared with the existing Java Seismicity map from the 2017 PuSGeN catalog. Clustering results using DBSCAN obtained a number of clusters of 2 to 6 clusters with the lowest Silhouette Coefficient value is 0.270 for the T5_2017-2021 period and the highest is 0.499 for the T1_2020 period.

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Section: Penelitian Terkaitunclassified

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Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN

Arafat,

Hariyadi,

Santoso

et al. 2023

JTIIK

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“…One of the most commonly used density-based clustering algorithms is density-based spatial clustering of applications with noise (DBSCAN). This algorithm was proposed in 1996 [9] and is still widely used today [43][44][45][46]. One of the advantages of this method is the ability to extract clusters of arbitrary shape in dense regions and recognize noise points.…”

Section: Density-based Spatial Clustering Of Applications With Noisementioning

confidence: 99%

Induced Seismicity and Detailed Fracture Mapping as Tools for Evaluating HDR Reservoir Volume

Węglińska

Leśniak

2021

Energies

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The main goal of this paper was to estimate the heat exchange rock mass volume of a hot dry rock (HDR) geothermal reservoir based on microseismicity location. There are two types of recorded microseismicity: induced by flowing fluid (wet microseismicity) and induced by stress mechanisms (dry microseismicity). In this paper, an attempt was made to extract events associated with the injected fluid flow. The authors rejected dry microseismic events with no hydraulic connection with the stimulated fracture network so as to avoid overestimating the reservoir volume. The proposed algorithm, which includes the collapsing method, automatic cluster detection, and spatiotemporal cluster evolution from the injection well, was applied to the microseismic dataset recorded during stimulation of the Soultz-sous-Forets HDR field in September 1993. The stimulated reservoir volume obtained from wet seismicity using convex hulls is approximately five times smaller than the volume obtained from the primary cloud of located events.

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