Identification of Earthquake Prone Zones in Sumatra using Density Based Spatial Clustering of Applications with Noise

Dwi Agustin Nuriani Sirodj, Muhammad Nur Aidi, Bagus Sartono, Utami Dyah Syafitri, Bayu Pranata

Abstract


This study investigates the spatial distribution of earthquakes in Sumatra using the DBSCAN clustering algorithm applied to seismic data spanning 1 January 2000 to 31 December 2023. The analysis identified two distinct seismic clusters: one in the northern region (Aceh and North Sumatra) and another in the southern region (Lampung, Bengkulu, and West Sumatra), while several events in central areas were classified as noise. Cluster validity assessment confirmed that the identified groups are compact and well separated, reflecting meaningful seismotectonic segmentation. Statistical testing further revealed significant differences in earthquake depth and magnitude between the clusters, supporting the robustness of the findings. Notably, the southern cluster corresponds to the Mentawai Fault system, whereas the northern cluster aligns with the subduction zone and the Sumatran Fault. DBSCAN proved particularly effective in this context as it can capture clusters of arbitrary shapes, consistent with the complex geological structures governing seismicity in Sumatra.

Keywords


DBSCAN; Earthquake Prone Zones; Spatial Clustering; Sumatra Seismic Risk

Full Text:

PDF

References


[1] J. Aldstadt, “Spatial clustering,” in Handbook of Applied Spatial Analysis, 2010, pp. 279–300. doi: 10.1007/978-3-642-03647-7_15.

[2] C. Neethu and S. Surendran, “Review of spatial clustering methods,” International Journal of Information Technology Infrastructure, vol. 2, 2013.

[3] S. Chawla, S. Shekhar, W. Wu, and U. Ozesmi, “Modeling spatial dependencies for mining geospatial data,” in Proceedings of the Western Marketing Education Association Conference, 2001, pp. 1–17. doi: 10.1137/1.9781611972719.27.

[4] B. Czecze and I. Bondár, “Hierarchical cluster analysis and multiple event relocation of seismic event clusters in Hungary between 2000 and 2016,” Journal of Seismology, vol. 23, pp. 1313–1326, 2019. doi: 10.1007/s10950-019-09868-5.

[5] A. Gupta, H. Sharma, and A. Akhtar, “A comparative analysis of k-means and hierarchical clustering,” EPRA International Journal of Multidisciplinary Research (IJMR), 2021. doi: 10.36713/epra2013.

[6] H. Nashir, A. Kurnia, and A. Fitrianto, “Subdistrict clustering in West Java Province based on disease incidence of JKN participants primary services,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, pp. 295–304, 2023. doi: 10.30598/BAREKENGVOL17ISS1PP0295-0304.

[7] A. Famalika and P. R. Sihombing, “Implementation of k-means and k-medians clustering in several countries based on global innovation index (GII) 2018,” Advance Sustainable Science, Engineering and Technology, vol. 3, p. 0210107, 2021. doi: 10.26877/asset.v3i1.8461.

[8] R. A. Ramadhan, D. Swanjaya, and R. Helilintar, “Optimizing predictive accuracy: A study of k-medoids and backpropagation for MPX2 oil sales forecasting,” Advance Sustainable Science, Engineering and Technology, vol. 6, p. 02401010, 2024. doi: 10.26877/asset.v6i1.17665.

[9] M. N. Aidi, T. R. Aditra, F. Ernawati, N. Nurjanah, E. Efriwati, E. D. Julianti, et al., “Clustering of communicable diseases in Indonesia and the factors that affect them: 2018 basic health research data statistical review,” Journal of Population and Social Studies, vol. 33, pp. 88–109, 2024. doi: 10.25133/JPSSV332025.005.

[10] M. Bariklana and A. Fauzan, “Implementation of the DBSCAN method for cluster mapping of earthquake spread location,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, pp. 867–878, 2023. doi: 10.30598/BAREKENGVOL17ISS2PP0867-0878.

[11] A. Amalia, U. Harmoko, and G. Yuliyanto, “Clustering of seismicity in the Indonesian region for the 2018–2020 period using the DBSCAN algorithm,” Journal of Physics and Its Applications, vol. 4, pp. 1–6, 2021. doi: 10.14710/JPA.V4I1.11884.

[12] M. T. Anwar, W. Hadikurniawati, E. Winarno, and A. Supriyanto, “Wildfire risk map based on DBSCAN clustering and cluster density evaluation,” Advance Sustainable Science, Engineering and Technology, vol. 1, p. 0190102, 2019. doi: 10.26877/asset.v1i1.4876.

[13] R. J. G. B. Campello, P. Kröger, J. Sander, and A. Zimek, “Density-based clustering,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 2, e1343, 2020. doi: 10.1002/widm.1343.

[14] A. Starczewski, M. M. Scherer, W. Ksiek, M. Dębski, and L. Wang, “A novel grid-based clustering algorithm,” Journal of Artificial Intelligence and Soft Computing Research, vol. 11, pp. 319–330, 2021. doi: 10.2478/JAISCR-2021-0019.

[15] P. R. Cummins, “Geohazards in Indonesia: Earth science for disaster risk reduction - introduction,” in Geological Society Special Publications, vol. 441, 2017, pp. 1–7. doi: 10.1144/SP441.1.

[16] A. B. Jatmiko, R. Ghaniswati, F. V. P. E. Utami, M. Burhan, C. A. Ardania, and A. K. Wulandari, Statistik Indonesia dalam Infografis, 8th ed. Badan Pusat Statistik, 2024.

[17] I. N. Sari and T. Prastowo, “Analisis seismisitas dan potensi bahaya bencana seismik di wilayah selatan Pulau Sumatera,” Inovasi Fisika Indonesia, vol. 11, pp. 12–19, 2022. doi: 10.26740/IFI.V11N2.P12-19.

[18] M. Shafapourtehrany, P. Yariyan, H. Özener, B. Pradhan, and F. Shabani, “Evaluating the application of k-mean clustering in earthquake vulnerability mapping of Istanbul, Turkey,” International Journal of Disaster Risk Reduction, vol. 79, p. 103154, 2022. doi: 10.1016/j.ijdrr.2022.103154.

[19] X. Shang, X. Li, A. Morales-Esteban, G. Asencio-Cortés, and Z. Wang, “Data field-based k-means clustering for spatio-temporal seismicity analysis and hazard assessment,” Remote Sensing, vol. 10, p. 461, 2018. doi: 10.3390/rs10030461.

[20] P. Novianti, D. Setyorini, and U. Rafflesia, “K-means cluster analysis in earthquake epicenter clustering,” International Journal of Advances in Intelligent Informatics, vol. 3, pp. 81–89, 2017. doi: 10.26555/ijain.v3i2.100.

[21] D. T. Trugman and P. M. Shearer, “Growclust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences,” Seismological Research Letters, vol. 88, pp. 379–391, 2017. doi: 10.1785/0220160188.

[22] K. Khan, S. U. Rehman, K. Aziz, S. Fong, S. Sarasvady, and A. Vishwa, “DBSCAN: Past, present and future,” in 5th International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), 2014, pp. 232–238. doi: 10.1109/ICADIWT.2014.6814687.

[23] T. Ali, S. Asghar, and N. A. Sajid, “Critical analysis of DBSCAN variations,” in International Conference on Information and Emerging Technologies (ICIET), 2010. doi: 10.1109/ICIET.2010.5625720.

[24] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226–231. Available online.

[25] L. Irawan, L. H. Hasibuan, and F. Fauzi, “Analisa prediksi efek kerusakan gempa dari magnitudo (skala Richter) dengan metode algoritma ID3 menggunakan aplikasi data mining Orange,” Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 14, pp. 189–201, 2020. doi: 10.47111/jti.v14i2.1079.

[26] A. Arimuko, S. Rohadi, and A. S. Rahman, “Seismotectonic studies to determine the recurrence of earthquakes Mw > 7 using a statistical approach and plate motion in the megathrust western part of Java,” Geotechnical and Geological Engineering, vol. 41, pp. 1397–1406, 2023. doi: 10.1007/s10706-022-02342-z.

[27] N. A. Galina, V. V. Bykova, R. N. Vakarchuk, and R. E. Tatevosian, “Effect of earthquake catalog declustering on seismic hazard assessment,” Seismic Instruments, vol. 55, pp. 59–69, 2019. doi: 10.3103/S0747923919010079.

[28] R. Wilson and A. Din, “Calculating varying scales of clustering among locations,” Cityscape: A Journal of Policy Development and Research, pp. 215–231, 2018.

[29] B. N. Boots and A. Getls, Point Pattern Analysis. Regional Research Institute, West Virginia University, 2020.

[30] Y. Chen, “An analytical process of spatial autocorrelation functions based on Moran’s index,” PLoS One, vol. 16, no. 3, e0249589, 2021. doi: 10.1371/journal.pone.0249589.

[31] L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Inc., 1990. doi: 10.1002/9780470316801.

[32] Juellyan, B. Setiawan, M. Hasan, H. Yunita, M. Sungkar, and T. Saidi, “Comparing Gardner-Knopoff, Gruenthal, and Uhrhammer earthquake declustering methods in Aceh, Indonesia,” in IOP Conference Series: Earth and Environmental Science, vol. 1245, 2023, p. 012010. doi: 10.1088/1755-1315/1245/1/012010.

[33] A. Sharma, R. K. Gupta, and A. Tiwari, “Improved density based spatial clustering of applications of noise clustering algorithm for knowledge discovery in spatial data,” Mathematical Problems in Engineering, vol. 2016, p. 1564516, 2016. doi: 10.1155/2016/1564516.

[34] A. N. Aulia, J. Jatnika, D. Arisa, M. Ramdhan, A. Patria, and L. Handayani, “Earthquake relocation and deformation analysis on the Mentawai segment of the Sumatra subduction zone,” in IOP Conference Series: Earth and Environmental Science, vol. 1437, 2024, p. 012023. doi: 10.1088/1755-1315/1437/1/012023.

[35] Tim Pusat Studi Gempa Nasional, Peta sumber dan bahaya gempa Indonesia tahun 2017. Kabupaten Bandung: Pusat Penelitian dan Pengembangan Perumahan dan Permukiman, Kementerian PUPR, 2017, vol. 1.

[36] M. M. Mukti, S. C. Singh, I. Deighton, N. D. Hananto, R. Moeremans, and H. Permana, “Structural evolution of backthrusting in the Mentawai fault zone, offshore Sumatran forearc,” Geochemistry, Geophysics, Geosystems, vol. 13, p. 12006, 2012. doi: 10.1029/2012GC004199.

[37] M. Madlazim and N. I. D. Lestari, “Analisis seismisitas dan potensi bahaya bencana seismik Pulau Sumatera berdasarkan data gempa 1970–2020,” Inovasi Fisika Indonesia, vol. 11, pp. 1–11, 2022. doi: 10.26740/IFI.V11N02.P1-11.




DOI: https://doi.org/10.18860/cauchy.v10i2.36120

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Dwi Agustin Nuriani Sirodj, Muhammad Nur Aidi, Bagus Sartono, Utami Dyah Syafitri, Bayu Pranata

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Editorial Office
Mathematics Department,
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Gajayana Street 50 Malang, East Java, Indonesia 65144
Faximile (+62) 341 558933
e-mail: cauchy@uin-malang.ac.id

Creative Commons License
CAUCHY: Jurnal Matematika Murni dan Aplikasi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.