Abstract
This paper presents a spatio-temporal binary grid-based clustering model for determining complex earthquake clusters with different shapes and heterogeneous densities present in a catalog. The 3D occurrence of earthquakes is mapped into a 2D-low memory sparse matrix through a grid mechanism in the binary domain with consideration of spatio-temporal attributes. Then, image-transformation of a non-empty sets binary feature matrix, a clustering strategy is implemented with logical AND operator as similarity measure among the binary vectors. This approach is applied to solve the problem of seismicity declustering which separates the clustering and non-clustering patterns of seismicity for real-world earthquake catalogs of Japan (1972–2020) and Eastern Mediterranean (1966–2020). Results demonstrate that the proposed method has a significant reduction in both computation and memory footprint with few tuning parameters. Background earthquakes have an impression on the homogeneous Poisson process with fair memory-less characteristics in the time domain as evident from graphical and statistical analysis. Overall seismicity and observed background activity both have similar multi-fractal behavior with a deviation of \(\pm 0.04\). The comparative analysis is carried out with benchmark declustering models: Gardner–Knopoff, Uhrhammer, Gruenthal window-based method, and Reasenberg’s approach, and superior performance of the proposed method is found in most cases.
Similar content being viewed by others
Data availability
The earthquake catalogs used in this study are publicly available at the official website of the United States Geological Survey [https://earthquake.usgs.gov/earthquakes/search/].
References
Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA (2022) A comprehensive survey of clustering algorithms: state-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell 110:104743
Cao L, Zhao Z, Wang D (2023) Clustering algorithms. In: target recognition and tracking for millimeter wave radar in intelligent transportation, pp 97–122. Springer
Gurunlu B, Ozturk S (2022) Efficient approach for block-based copy-move forgery detection. In: Smart trends in computing and communications: proceedings of smartcom 2021, pp 167–174. Springer
Balaha HM, Antar ER, Saafan MM, El-Gendy EM (2023) A comprehensive framework towards segmenting and classifying breast cancer patients using deep learning and aquila optimizer. J Ambient Intell Human Comput 14(6):7897–7917
Krishnappa SG, Reddy KUK (2023) Breast cancer classification from histopathological images using dual deep network architecture. J Ambient Intell Human Comput 14(6):7885–7896
Gupta V (2023) Application of chaos theory for arrhythmia detection in pathological databases. Int J Med Eng Inf 15(2):191–202
Murali P, Revathy R, Balamurali S, Tayade A (2020) Integration of rnn with garch refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach. J Ambient Intell Human Comput, 1–13
Sengar S, Liu X (2020) Ensemble approach for short term load forecasting in wind energy system using hybrid algorithm. J Ambient Intell Human Comput 11:5297–5314
Nayak JR, Shaw B, Sahu BK (2023) A fuzzy adaptive symbiotic organism search based hybrid wavelet transform-extreme learning machine model for load forecasting of power system: a case study. J Ambient Intell Human Comput 14(8):10833–10847
Saad OM, Hafez AG, Soliman MS (2020) Deep learning approach for earthquake parameters classification in earthquake early warning system. IEEE Geosci Remote Sens Lett 18(7):1293–1297
Wang C-Y, Huang T-C, Wu Y-M (2022) Using LSTM neural networks for onsite earthquake early warning. Seismol Soc Am 93(2A):814–826
Taylor NC, Kruger K, Bekker A (2023) A human cyber-physical system for human-centered computing in seafaring. J Ambient Intell Human Comput 14(6):7871–7884
Sahoh B, Choksuriwong A (2023) The role of explainable artificial intelligence in high-stakes decision-making systems: a systematic review. J Ambient Intell Human Comput 14(6):7827–7843
Maitre J, Bouchard K, Gaboury S (2023) Data filtering and deep learning for enhanced human activity recognition from UWB radars. J Ambient Intell Human Comput 14(6):7845–7856
Berhich A, Belouadha F-Z, Kabbaj MI (2023) An attention-based LSTM network for large earthquake prediction. Soil Dyn Earthq Eng 165:107663
Kagan YY, Jackson DD (1991) Long-term earthquake clustering. Geophys J Int 104(1):117–133
Utsu T, Ogata Y, Ritsuko S et al (1995) The centenary of the OMORI formula for a decay law of aftershock activity. J Phys Earth 43(1):1–33
Omori F (1894) On the after-shocks of earthquakes vol 7. The University
Utsu T (1957) Magnitude of earthquakes and occurrence of their aftershocks. Zisin 10:35–45
Wang Q, Guo Y, Yu L, Li P (2017) Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. IEEE Transact Emerg Top Comput 8(1):148–158
Asencio-Cortes G, Martinez-Alvarez F, Morales-Esteban A, Reyes J (2016) A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction. Knowl Based Syst 101:15–30
Du H, Zhou Y, Ma Y, Wang S (2021) Astrologer: exploiting graph neural hawkes process for event propagation prediction with spatio-temporal characteristics. Knowl Based Syst 228:107247
Agrawal K, Garg S, Sharma S, Patel P (2016) Development and validation of optics based spatio-temporal clustering technique. Inf Sci 369:388–401
Rocha JAM, Times VC, Oliveira G, Alvares LO, Bogorny V (2010) Db-smot: a direction-based spatio-temporal clustering method. In: 2010 5th IEEE international conference intelligent systems, pp 114–119. IEEE
Fitrianah D, Hidayanto AN, Fahmi H, Gaol JL, Arymurthy AM (2015) ST-agrid: a spatio temporal grid density based clustering and its application for determining the potential fishing zones. Int J Softw Eng Appl 9(1):13–26
Kisilevich S, Mansmann F, Nanni M, Rinzivillo S (2009) Spatio-temporal clustering. In: Data mining and knowledge discovery handbook, pp 855–874. Springer
Georgoulas G, Konstantaras A, Katsifarakis E, Stylios CD, Maravelakis E, Vachtsevanos GJ (2013) seismic-mass density-based algorithm for spatio-temporal clustering. Exp Syst Appl 40(10):4183–4189
Nanda SJ, Panda G (2015) Design of computationally efficient density-based clustering algorithms. Data Knowl Eng 95:23–38
Ansari A, Firuzi E, Etemadsaeed L (2015) Delineation of seismic sources in probabilistic seismic-hazard analysis using fuzzy cluster analysis and monte carlo simulation. Bull Seismol Soc Am 105(4):2174–2191
Rehman K, Burton PW, Weatherill GA (2014) K-means cluster analysis and seismicity partitioning for Pakistan. J Seismol 18(3):401–419
Morales-Esteban A, Martinez-Alvarez F, Scitovski S, Scitovski R (2014) A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning. Comput Geosci 73:132–141
Morales-Esteban A, Martinez-Alvarez F, Troncoso A, Justo J, Rubio-Escudero C (2010) Pattern recognition to forecast seismic time series. Exp Syst Appl 37(12):8333–8342
Ida Y, Ishida M (2022) Analysis of seismic activity using self-organizing map: implications for earthquake prediction. Pure Appl Geophys 179(1):1–9
Telesca L, Thai AT, Lovallo M, Cao DT (2022) Visibility graph analysis of reservoir-triggered seismicity: the case of song tranh 2 hydropower, vietnam. Entropy 24(11):1620
Malakar S, Rai AK (2022) Seismicity clusters and vulnerability in the Himalayas by machine learning and integrated MCDM models. Arab J Geosci 15(22):1674
Golay J, Kanevski M, Orozco CDV, Leuenberger M (2014) The multipoint Morisita index for the analysis of spatial patterns. Physica A Stat Mech Appl 406:191–202
Corral A (2006) Dependence of earthquake recurrence times and independence of magnitudes on seismicity history. Tectonophysics 424(3–4):177–193
Poulos A, Monsalve M, Zamora N, de la Llera JC (2019) An updated recurrence model for chilean subduction seismicity and statistical validation of its poisson nature. Bull Seismol Soc Am 109(1):66–74
Talbi A, Nanjo K, Satake K, Zhuang J, Hamdache M (2013) Comparison of seismicity declustering methods using a probabilistic measure of clustering. J Seismol 17(3):1041–1061
Batac R, Kantz H (2014) Observing spatio-temporal clustering and separation using interevent distributions of regional earthquakes. Nonlinear Process Geophys 21(4):735–744
Van Stiphout T, Zhuang J, Marsan D (2012) Seismicity declustering. Commun Online Resourc Stat Seism Anal. https://doi.org/10.5078/corssa-52382934
Azak TE, Kalafat D, Sesetyan K, Demircioglu M (2018) Effects of seismic declustering on seismic hazard assessment: a sensitivity study using the turkish earthquake catalogue. Bull Earthq Eng 16(8):3339–3366
Petersen MD, Moschetti MP, Powers PM, Mueller CS, Haller KM, Frankel AD, Zeng Y, Rezaeian S, Harmsen SC, Boyd OS et al (2015) The 2014 united states national seismic hazard model. Earthq Spectra 31(1–suppl):1–30
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 Transact Geosci Remote Sens 52(9):5443–5453
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(1):57–65
Gardner J, Knopoff L (1974) Is the sequence of earthquakes in southern California, with aftershocks removed, poissonian? Bull Seismol Soc Am 64(5):1363–1367
Reasenberg P (1985) Second-order moment of central California seismicity, 1969–1982. J Geophys Res Solid Earth 90(B7):5479–5495
Zhuang J, Ogata Y, Vere-Jones D (2002) Stochastic declustering of space-time earthquake occurrences. J Am Stat Assoc 97(458):369–380
Zaliapin I, Gabrielov A, Keilis-Borok V, Wong H (2008) Clustering analysis of seismicity and aftershock identification. Phys Rev Lett 101(1):018501
Vijay RK, Nanda SJ (2017) Tetra-stage cluster identification model to analyse the seismic activities of Japan, Himalaya and Taiwan. IET Signal Process 12(1):95–103
Vijay RK, Nanda SJ (2019) A variable e-DBSCAN algorithm for declustering earthquake catalogs. In: Soft computing for problem solving, pp 639–651. Springer
Vijay RK, Nanda SJ (2019) Shared nearest neighborhood intensity based declustering model for analysis of spatio-temporal seismicity. IEEE J Select Top Appl Earth Observ Remote Sens 12(5):1619–1627
Vijay RK, Nanda SJ (2021) Seismicity analysis using space-time density peak clustering method. Pattern Anal Appl 24(1):181–201
Sharma A, Nanda SJ, Vijay RK (2021) A model based on fuzzy c-means with density peak clustering for seismicity analysis of earthquake prone regions. In: Soft computing for problem solving: proceedings of SocProS 2020, vol 2, pp 173–185. Springer
Sharma A, Vijay RK, Nanda SJ (2023) Identification and spatio-temporal analysis of earthquake clusters using som-dbscan model. Neural Comput Appl 35(11):8081–8108
Aden-Antoniow F, Frank W, Seydoux L (2022) An adaptable random forest model for the declustering of earthquake catalogs. J Geophys Res Solid Earth 127(2):2021–023254
Vijay RK, Nanda SJ (2022) Sliding temporal window-based feature extraction with k-means clustering for zagros (iran) seismicity analysis. In: 2022 international conference on connected systems & intelligence (CSI), pp 1–10. IEEE
Cho N, Tiampo K, Bhattacharya P, Shcherbakov R, Chen C, Li H, Klein W (2010) Declustering seismicity using the thirumalai-mountain metric. In: AGU fall meeting abstracts
Vijay R, Nanda SJ (2017) Declustering of an earthquake catalog based on ergodicity using parallel grey wolf optimization. In: IEEE congress on evolutionary computation (CEC), pp 1667–1674
Vijay RK, Nanda SJ (2019) A quantum grey wolf optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework. J Comput Sci 36:101019
Sharma A, Nanda SJ, Vijay RK (2021) A binary NSGA-ii model for de-clustering seismicity of turkey and Chile. In: 2021 IEEE congress on evolutionary computation (CEC), pp 981–988. IEEE
Li M, Stolz M, Feng Z, Kunert M, Henze R, Kuckay F (2018) An adaptive 3D grid-based clustering algorithm for automotive high resolution radar sensor. In: 2018 IEEE International conference on vehicular electronics and safety (ICVES), pp 1–7. IEEE
Chen J, Lin X, Xuan Q, Xiang Y (2019) Fgch: a fast and grid based clustering algorithm for hybrid data stream. Appl Intell 49:1228–1244
Starczewski A, Scherer MM, Ksiazek W, Debski M, Wang L (2021) A novel grid-based clustering algorithm. J Artif Intell Soft Comput Res 11
Catalog SE USGS. https://earthquake.usgs.gov/earthquakes/search/ [Accessed: (November 22, 2022)]
Sornette D, Sornette A (1999) General theory of the modified Gutenberg–Richter law for large seismic moments. Bull Seismol Soc Am 89(4):1121–1130
Mignan A (2012) Functional shape of the earthquake frequency-magnitude distribution and completeness magnitude. J Geophys Res: Solid Earth 117(B8)
Goh K-I, Barabasi A-L (2008) Burstiness and memory in complex systems. EPL (Europhysics Letters) 81(4):48002
Uhrhammer R (1986) Characteristics of northern and central California seismicity. Earthq Notes 57(1):21
Wiemer S (2001) A software package to analyze seismicity: Zmap. Seismol Res Lett 72(3):373–382
Birant D, Kut A (2007) St-dbscan: an algorithm for clustering spatial-temporal data. Data Knowl Eng 60(1):208–221
Wang M, Wang A, Li A (2006) Mining spatial-temporal clusters from geo-databases. In: International conference on advanced data mining and applications, pp 263–270. Springer
Joshi D, Samal A, Soh L-K (2013) Spatio-temporal polygonal clustering with space and time as first-class citizens. GeoInformatica 17(2):387–412
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We have no conflict of interest to declare that is relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Vijay, R.K., Nanda, S.J. & Sharma, A. A spatio-temporal binary grid-based clustering model for seismicity analysis. Pattern Anal Applic 27, 14 (2024). https://doi.org/10.1007/s10044-024-01234-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10044-024-01234-7