Abstract
The occurrences of earthquakes have no any explicit warning, and earthquake magnitude prediction is still extremely challenging now. Therefore, this study proposes a seismic prediction model based on clustering of global earthquake data. First, an improved K-means clustering algorithm for global earthquake catalogs is proposed. Traditional K-means clustering has several limitations, i.e., the number of clusters needs to be initialized, the initial cluster centers are arbitrarily selected, and there is currently no magnitude parameter in the K-means clustering algorithm. To improve the algorithm, this study employs the space–time–magnitude (STM) distance and then proposes a maximum–minimum STM distance for the selection of the initial cluster centers. Additionally, the sum of squares error, Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient are applied to determine the number of clusters. Subsequently, a seismic prediction model based on the clustering result combined with an artificial neural network is presented. Application of the improved clustering algorithm to the global seismic catalog from 1900–2019 obtained from the United States Geological Survey reveals better clustering accuracy than the traditional K-means algorithm and is also effective for seismic risk analysis in the local region. Furthermore, the seismic prediction model based on the clustering result also exhibits good performance, which has practical significance and reference value for future predictions of earthquake magnitude.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alexandridis A, Chondrodima E, Efthimiou E, Papadakis G, Vallianatos F, Triantis D (2013) Large earthquake occurrence estimation based on radial basis function neural networks. IEEE Trans Geosci Remote Sens 52:5443–5453. https://doi.org/10.1109/TGRS.2013.2288979
Alves EI (2006) Earthquake forecasting using neural networks: results and future work. Nonlinear Dyn 44:341–349. https://doi.org/10.1007/s11071-006-2018-1
Asencio-Cortés G, Martínez-Álvarez F, Morales-Esteban A, et al (2015) Improving earthquake prediction with principal component analysis: application to Chile. In: International Conference on Hybrid Artificial Intelligence Systems. Springer, pp 393–404. 10.1007/978-3-319-19644-2_33
Asencio-Cortés G, Martínez-Álvarez F, Troncoso A, Morales-Esteban A (2017) Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks. Neural Comput & Applic 28:1043–1055. https://doi.org/10.1007/s00521-015-2121-7
Asencio-Cortés G, Morales Esteban A, Shang X, Martínez Álvarez F (2018) Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput Geosci 115:198–210. https://doi.org/10.1016/j.cageo.2017.10.011
Asim KM, Martínez-Álvarez F, Basit A, Iqbal T (2017) Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat Hazards 85:471–486. https://doi.org/10.1007/s11069-016-2579-3
Asim KM, Idris A, Iqbal T, Martínez-Álvarez F (2018) Seismic indicators based earthquake predictor system using genetic programming and AdaBoost classification. Soil Dyn Earthq Eng 111:1–7. https://doi.org/10.1016/j.soildyn.2018.04.020
Asim KM, Moustafa SS, Niaz IA et al (2020) Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in Cyprus. Soil Dyn Earthq Eng 130:105932. https://doi.org/10.1016/j.soildyn.2019.105932
Baiesi M, Paczuski M (2004) Scale-free networks of earthquakes and aftershocks. Phys Rev E 69:066106. https://doi.org/10.1103/PhysRevE.69.066106
Båth M (1965) Lateral inhomogeneities of the upper mantle. Tectonophysics 2:483–514. https://doi.org/10.1016/0040-1951(65)90003-X
Burton PW, Weatherill G, Karnawati D, Pramumijoyo S (2008) Seismic hazard assessment and zoning in Java: new and alternative probabilistic assessment models. In: International Conference on Earthquake Engineering and Disaster Mitigation. pp 2834–291
Ebel JE, Chambers DW, Kafka AL, Baglivo JA (2007) Non-Poissonian earthquake clustering and the hidden Markov model as bases for earthquake forecasting in California. Seismol Res Lett 78:57–65. https://doi.org/10.1785/gssrl.78.1.57
Florido E, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Aznarte-Mellado JL (2015) Detecting precursory patterns to enhance earthquake prediction in Chile. Comput Geosci 76:112–120. https://doi.org/10.1016/j.cageo.2014.12.002
Galkina A, Grafeeva N (2019) Machine learning methods for earthquake prediction: a survey. In: Proceedings of the Fourth Conference on Software Engineering and Information Management (SEIM-2019). Saint Petersburg, Russia, p 25
Gutenberg B, Richter CF (1936) Magnitude and energy of earthquakes. Science 83:183–185. https://www.jstor.org/stable/1662411. Accessed 14 March 2021
Ikram A, Qamar U (2015) Developing an expert system based on association rules and predicate logic for earthquake prediction. Knowl-Based Syst 75:87–103. https://doi.org/10.1016/j.knosys.2014.11.024
Kagan YY, Jackson DD, Rong Y (2007) A testable five-year forecast of moderate and large earthquakes in Southern California based on smoothed seismicity. Seismol Res Lett 78:94–98. https://doi.org/10.1785/gssrl.78.1.94
Kuyuk HS, Yildirim E, Dogan E, Horasan G (2012) Application of k-means and Gaussian mixture model for classification of seismic activities in Istanbul. Nonlinear Process Geophys 19(4):411–419. https://doi.org/10.5194/npg-19-411-2012
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA, pp 281–297. http://projecteuclid.org/euclid.bsmsp/1200512992, 2018. Accessed 14 March 2021
Martínez-Álvarez F, Troncoso A, Morales-Esteban A, Riquelme JC (2011) Computational intelligence techniques for predicting earthquakes. In: International Conference on Hybrid Artificial Intelligence Systems. Springer, pp 287–294. 10.1007/978-3-642-21222-2_35
Michael AJ, Werner MJ (2018) Preface to the focus section on the Collaboratory for the Study of Earthquake Predictability (CSEP): new results and future directions. Seismol Res Lett 89:1226–1228. https://doi.org/10.1785/0220180161
Mignan A, Werner MJ, Wiemer S, Chen CC, Wu YM (2011) Bayesian estimation of the spatially varying completeness magnitude of earthquake catalogs. Bull Seismol Soc Am 101:1371–1385. https://doi.org/10.1785/0120100223
Mirrashid M (2014) Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Nat Hazards 74:1577–1593. https://doi.org/10.1007/s11069-014-1264-7
Morales-Esteban A, Martínez-Álvarez F, Reyes J (2013) Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 593:121–134. https://doi.org/10.1016/j.tecto.2013.02.036
Morales-Esteban A, Martínez-Álvarez F, Scitovski S, Scitovski R (2014) A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning. Comput Geosci 73:132–141. https://doi.org/10.1016/j.cageo.2014.09.003
Moustra M, Avraamides M, Christodoulou C (2011) Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert Syst Appl 38:15032–15039. https://doi.org/10.1016/j.eswa.2011.05.043
Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neural Syst 17:13–33. https://doi.org/10.1142/S0129065707000890
Panakkat A, Adeli H (2009) Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput Aided Civ Infrastruct Eng 24:280–292. https://doi.org/10.1111/j.1467-8667.2009.00595.x
Pearson Education, Inc (2011) Earthquakes and earthquake hazards Earth, 10e - Chapter 11. https://slideplayer.com/slide/7584523/.Accessed 18 January 2021
Petersen M, Moschetti M, Powers P et al (2014) Documentation for the 2014 Update of the United States National Seismic Hazard Maps. USGS Open-File Rep:2014–1091. https://doi.org/10.3133/ofr20141091
Rehman K, Burton PW, Weatherill GA (2014) K-means cluster analysis and seismicity partitioning for Pakistan. J Seismol 18:401–419. https://doi.org/10.1007/s10950-013-9415-y
Reyes J, Morales-Esteban A, Martínez-Álvarez F (2013) Neural networks to predict earthquakes in Chile. Appl Soft Comput 13:1314–1328. https://doi.org/10.1016/j.asoc.2012.10.014
Shen Z-K, Jackson DD, Kagan YY (2007) Implications of geodetic strain rate for future earthquakes, with a five-year forecast of M5 earthquakes in Southern California. Seismol Res Lett 78:116–120. https://doi.org/10.1785/gssrl.78.1.116
Špičák A, Vaněk J (2016) Earthquake swarms reveal submarine magma unrest induced by distant mega-earthquakes: Andaman Sea region. J Asian Earth Sci 116:155–163. https://doi.org/10.1016/j.jseaes.2015.11.017
USGS (2019) Search Earthquake Catalog. https://earthquake.usgs.gov/earthquakes/search/. Accessed 27 March 2019
Utsu T (1961) A statistical study on the occurrence of aftershocks. Geophys Mag 30:521–605
Wang J, Su X (2011) An improved K-means clustering algorithm. In: 2011 IEEE 3rd International Conference on Communication Software and Networks. IEEE, pp 44–46. 10.1109/ICCSN.2011.6014384
Weatherill G, Burton PW (2009) Delineation of shallow seismic source zones using K-means cluster analysis, with application to the Aegean region. Geophys J Int 176:565–588. https://doi.org/10.1111/j.1365-246X.2008.03997.x
Weatherill G, Burton PW (2010) An alternative approach to probabilistic seismic hazard analysis in the Aegean region using Monte Carlo simulation. Tectonophysics 492:253–278. https://doi.org/10.1016/j.tecto.2010.06.022
Wiemer S (2001) A software package to analyze seismicity: ZMAP. Seismol Res Lett 72:373–382. https://doi.org/10.1785/gssrl.72.3.373
Wiemer S, Wyss M (2000) Minimum magnitude of completeness in earthquake catalogs: examples from Alaska, the western United States, and Japan. Bull Seismol Soc Am 90:859–869. https://doi.org/10.1785/0119990114
Yin L, Li X, Zheng W, Yin Z, Song L, Ge L, Zeng Q (2019) Fractal dimension analysis for seismicity spatial and temporal distribution in the circum-Pacific seismic belt. J Earth Syst Sci 128:22. https://doi.org/10.1007/s12040-018-1040-2
Zaliapin I, Ben-Zion Y (2013) Earthquake clusters in southern California I: identification and stability. J Geophys Res Solid Earth 118:2847–2864. https://doi.org/10.1002/jgrb.50179
Zamani A, Sorbi MR, Safavi AA (2013) Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Sci Inf 6:71–85. https://doi.org/10.1007/s12145-013-0112-8
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yuan, R. An improved K-means clustering algorithm for global earthquake catalogs and earthquake magnitude prediction. J Seismol 25, 1005–1020 (2021). https://doi.org/10.1007/s10950-021-09999-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10950-021-09999-8