Comparison of K-Means Algorithm and DBSCAN on Aftershock Activity in the Flores Sea: Seismic Activity 2019-2022
DOI:
https://doi.org/10.56741/jnest.v2i03.393Keywords:
cluster method, DBSCAN, earthquake, K-MeansAbstract
This study seeks to determine whether the clustering method can be used to analyze Flores Sea earthquake activity. In this investigation, the BMKG Repo serves as the source for real earthquake vibration data collection. The stages of this research include preparing the data in CSV format and then preparing the data to eliminate useless data by identifying missing data. On the basis of the research data, it was determined that the K-Means and DBSCAN methods are used to determine the clustering method for analyzing earthquake activity. In addition, the data is depicted using a graphical Elbow method so that we can determine the number of clusters of aftershocks in the Flores Sea. The results of the visualization of aftershocks that followed earthquakes in the Flores Sea between 2019 and 2022 revealed three distinct groups of earthquake source depths: 33 to 70 kilometers, 150 to 300 kilometers, and 500 to 800 kilometers. In terms of the shilhoute index parameter, the K-Means algorithm is preferable to the DBSCAN algorithm when clustering results are used to analyze earthquake activity.
Downloads
References
A. Jufriansah et al., “Estimation of Flores Sea Aftershock Rupture Data Based on AI,” Indones. Rev. Phys, vol. 6, no. 1, pp. 46–56, 2023, doi: 10.12928/irip.v6i1.6705.
A. Jufriansah, A. Khusnani, Y. Pramudya, and M. Afriyanto, “Comparison of aftershock behavior of the flores sea 12 december 1992 and 14 december 2021,” Journal of Physics: Theories and Applications, vol. 7, no. 1, pp. 65-74., 2023. DOI: https://doi.org/10.20961/jphystheor-appl.v7i1.71609
Y. Liu, J. Zhuang, and C. Jiang, “Background Seismicity before and after the 1976 Ms 7.8 Tangshan Earthquake: Is Its Aftershock Sequence Still Continuing?,” Seismological Research Letters, vol. 92, no. 2A, pp. 877–885, Mar. 2021, doi: 10.1785/0220200179. DOI: https://doi.org/10.1785/0220200179
A. Khusnani, A. Jufriansah, and M. Afriyanto, “Utilization of Seismic Data as a Tsunami Vulnerability Review,” Indonesian Review of Physics, vol. 5, no. 2, pp. 66–72, 2022. DOI: https://doi.org/10.12928/irip.v5i2.6706
A. Jufriansah, Y. Pramudya, A. Khusnani, and S. Saputra, “Analysis of Earthquake Activity in Indonesia by Clustering Method,” Journal of Physics: Theories and Applications, vol. 5, no. 2, p. 92, Sep. 2021, doi: 10.20961/jphystheor-appl.v5i2.59133. DOI: https://doi.org/10.20961/jphystheor-appl.v5i2.59133
H. S. Kuyuk, E. Yildirim, E. Dogan, and G. Horasan, “Application of k-means and Gaussian mixture model for classification of seismic activities in Istanbul,” Nonlinear Process Geophys, vol. 19, no. 4, pp. 411–419, Aug. 2012, doi: 10.5194/npg-19-411-2012. DOI: https://doi.org/10.5194/npg-19-411-2012
Z. Sheikhhosseini, N. Mirzaei, R. Heidari, and H. Monkaresi, “Delineation of potential seismic sources using weighted K-means cluster analysis and particle swarm optimization (PSO),” Acta Geophysica, vol. 69, no. 6, pp. 2161–2172, Dec. 2021, doi: 10.1007/s11600-021-00683-6. DOI: https://doi.org/10.1007/s11600-021-00683-6
S. J. Nanda and G. Panda, “A survey on nature inspired metaheuristic algorithms for partitional clustering,” Swarm Evol Comput, vol. 16, pp. 1–18, Jun. 2014, doi: 10.1016/j.swevo.2013.11.003. DOI: https://doi.org/10.1016/j.swevo.2013.11.003
A. AlAli and F. Anifowose, “Seismic velocity modeling in the digital transformation era: a review of the role of machine learning,” J Pet Explor Prod Technol, vol. 12, no. 1, pp. 21–34, Jan. 2022, doi: 10.1007/s13202-021-01304-0. DOI: https://doi.org/10.1007/s13202-021-01304-0
A. SHİRAZY, A. HEZARKHANİ, A. SHİRAZİ, S. KHAKMARDAN, and R. ROOKİ, “K-Means Clustering and General Regression Neural Network Methods for Copper Mineralization probability in Char-Farsakh, Iran,” Türkiye Jeoloji Bülteni / Geological Bulletin of Turkey, Dec. 2021, doi: 10.25288/tjb.1010636. DOI: https://doi.org/10.25288/tjb.1010636
A. E. Ezugwu et al., “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects,” Eng Appl Artif Intell, vol. 110, p. 104743, Apr. 2022, doi: 10.1016/j.engappai.2022.104743. DOI: https://doi.org/10.1016/j.engappai.2022.104743
F. Ramdani, O. Kettani, and B. Tadili, “Evidence for subduction beneath Gibraltar Arc and Andean regions from k-means earthquake centroids,” J Seismol, vol. 19, no. 1, pp. 41–53, Jan. 2015, doi: 10.1007/s10950-014-9449-9. DOI: https://doi.org/10.1007/s10950-014-9449-9
R. K. Vijay and S. J. Nanda, “A Variable ε-DBSCAN Algorithm for Declustering Earthquake Catalogs,” 2019, pp. 639–651. doi: 10.1007/978-981-13-1592-3_50. DOI: https://doi.org/10.1007/978-981-13-1592-3_50
E. Amigó, J. Gonzalo, J. Artiles, and F. Verdejo, “A comparison of extrinsic clustering evaluation metrics based on formal constraints,” Inf Retr Boston, vol. 12, no. 4, pp. 461–486, Aug. 2009, doi: 10.1007/s10791-008-9066-8. DOI: https://doi.org/10.1007/s10791-008-9066-8
A. Fahad et al., “A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis,” IEEE Trans Emerg Top Comput, vol. 2, no. 3, pp. 267–279, Sep. 2014, doi: 10.1109/TETC.2014.2330519. DOI: https://doi.org/10.1109/TETC.2014.2330519
R. Maneno, B. J. Sentosa, and G. Rachman, “Relocation Of Earthquake Hypocenter In The Flores Region Using Hypo71,” IPTEK The Journal of Engineering, vol. 5, no. 2, May 2019, doi: 10.12962/j23378557.v5i2.a5024. DOI: https://doi.org/10.12962/joe.v5i2.5024
J. Beckers and T. Lay, “Very broadband seismic analysis of the 1992 Flores, Indonesia, earthquake (M w = 7.9),” J Geophys Res Solid Earth, vol. 100, no. B9, pp. 18179–18193, Sep. 1995, doi: 10.1029/95JB01689. DOI: https://doi.org/10.1029/95JB01689
I. R. Pranantyo and P. R. Cummins, “Multi-Data-Type Source Estimation for the 1992 Flores Earthquake and Tsunami,” Pure Appl Geophys, vol. 176, no. 7, pp. 2969–2983, 2019, doi: 10.1007/s00024-018-2078-4. DOI: https://doi.org/10.1007/s00024-018-2078-4
I. R. Pranantyo, M. Heidarzadeh, and P. R. Cummins, “Complex tsunami hazards in eastern Indonesia from seismic and non-seismic sources: Deterministic modelling based on historical and modern data,” Geosci Lett, vol. 8, no. 1, 2021, doi: 10.1186/s40562-021-00190-y.
I. R. Pranantyo, M. Heidarzadeh, and P. R. Cummins, “Complex tsunami hazards in eastern Indonesia from seismic and non-seismic sources: Deterministic modelling based on historical and modern data,” Geosci Lett, vol. 8, no. 1, p. 20, Dec. 2021, doi: 10.1186/s40562-021-00190-y. DOI: https://doi.org/10.1186/s40562-021-00190-y
F. Li, H. Sun, Y. Gu, and G. Yu, “A Noise-Aware Multiple Imputation Algorithm for Missing Data,” Mathematics, vol. 11, no. 1, p. 73, Dec. 2022, doi: 10.3390/math11010073. DOI: https://doi.org/10.3390/math11010073
M. Pazhoohesh, M. S. Javadi, M. Gheisari, S. Aziz, and R. Villa, “Dealing with Missing Data in the Smart Buildings using Innovative Imputation Techniques,” in IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Oct. 2021, pp. 1–7. doi: 10.1109/IECON48115.2021.9612650. DOI: https://doi.org/10.1109/IECON48115.2021.9612650
J. C. F. de Winter, S. D. Gosling, and J. Potter, “Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data.,” Psychol Methods, vol. 21, no. 3, pp. 273–290, Sep. 2016, doi: 10.1037/met0000079. DOI: https://doi.org/10.1037/met0000079
M.-T. Puth, M. Neuhäuser, and G. D. Ruxton, “Effective use of Pearson’s product–moment correlation coefficient,” Anim Behav, vol. 93, pp. 183–189, Jul. 2014, doi: 10.1016/j.anbehav.2014.05.003. DOI: https://doi.org/10.1016/j.anbehav.2014.05.003
S. Panigrahi and H. S. Behera, “Effect of Normalization Techniques on Univariate Time Series Forecasting using Evolutionary Higher Order Neural Network,” 2013. DOI: https://doi.org/10.5120/13146-0553
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl Soft Comput, vol. 97, p. 105524, Dec. 2020, doi: 10.1016/j.asoc.2019.105524. DOI: https://doi.org/10.1016/j.asoc.2019.105524
S. Kim, Y. Noh, Y.-J. Kang, S. Park, J.-W. Lee, and S.-W. Chin, “Hybrid data-scaling method for fault classification of compressors,” Measurement, vol. 201, p. 111619, Sep. 2022, doi: 10.1016/j.measurement.2022.111619. DOI: https://doi.org/10.1016/j.measurement.2022.111619
Z. Li, “A Generic Model of Global Earthquake Rupture Characteristics Revealed by Machine Learning,” Geophys Res Lett, vol. 49, no. 8, Apr. 2022, doi: 10.1029/2021GL096464. DOI: https://doi.org/10.1029/2021GL096464
L. Seydoux, R. Balestriero, P. Poli, M. de Hoop, M. Campillo, and R. Baraniuk, “Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning,” Nat Commun, vol. 11, no. 1, p. 3972, Aug. 2020, doi: 10.1038/s41467-020-17841-x. DOI: https://doi.org/10.1038/s41467-020-17841-x
K. J. Bergen, T. Chen, and Z. Li, “Preface to the Focus Section on Machine Learning in Seismology,” Seismological Research Letters, vol. 90, no. 2A, pp. 477–480, Mar. 2019, doi: 10.1785/0220190018. DOI: https://doi.org/10.1785/0220190018
S. M. Mousavi, W. L. Ellsworth, W. Zhu, L. Y. Chuang, and G. C. Beroza, “Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking,” Nat Commun, vol. 11, no. 1, p. 3952, Aug. 2020, doi: 10.1038/s41467-020-17591-w. DOI: https://doi.org/10.1038/s41467-020-17591-w
Z. Fan and X. Xu, “Application and visualization of typical clustering algorithms in seismic data analysis,” Procedia Comput Sci, vol. 151, pp. 171–178, 2019, doi: 10.1016/j.procs.2019.04.026. DOI: https://doi.org/10.1016/j.procs.2019.04.026
B. Pokharel, O. F. Althuwaynee, A. Aydda, S.-W. Kim, S. Lim, and H.-J. Park, “Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal,” Landslides, vol. 18, no. 4, pp. 1403–1419, Apr. 2021, doi: 10.1007/s10346-020-01558-5. DOI: https://doi.org/10.1007/s10346-020-01558-5
M. S. Boettcher, “Earthquake scaling relations for mid-ocean ridge transform faults,” J Geophys Res, vol. 109, no. B12, p. B12302, 2004, doi: 10.1029/2004JB003110. DOI: https://doi.org/10.1029/2004JB003110
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 Journal of Novel Engineering Science and Technology
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
