A Real time Integrated Framework for Seismic Activity Monitoring and Probabilistic Forecasting using Machine Learning and Geospatial Analytics: The EASO System
DOI:
https://doi.org/10.62760/iteecs.5.2.2026.200Keywords:
DBSCAN Clustering, TAS Modelling, Gutenberg-Richter Law, Open-Source Software, Python programming, REST API Integration, Seismic Swarms, Statistical Seismology, Visual ForensicsAbstract
This paper presents a robust, automated computational framework for the real time monitoring and statistical analysis of seismic activity, focusing on the seismically active region of Epirus, Greece. To address the inherent latencies of traditional monitoring, this study introduces the Epirus Advanced Seismic Observatory (EASO). Utilizing the Python programming language and integrating live data from the Euro Mediterranean Seismological Centre (EMSC) API, EASO implements an end to end pipeline for data acquisition, processing, and visualization. The methodology employs the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to autonomously identify seismogenic structures and spatial clusters without prior geological constraints. Furthermore, the framework integrates fundamental seismological laws, including the Gutenberg Richter magnitude frequency relationship and the Modified Omori Law (ETAS model), to estimate b-values and decay rates of aftershock sequences. A multi panel visualization dashboard was developed, providing a "full spectrum" probability forecast for various magnitude thresholds (4.0–7.0 Richter) within a seven day window. The results demonstrate that the integration of unsupervised machine learning with traditional stochastic models significantly enhances the interpretability of regional seismic swarms. This study concludes that EASO provides a critical tool for real time visual forensics, risk assessment, and public information, offering a scalable solution that can be adapted to other high risk tectonic zones globally.
References
H. Kanamori, “Real-time seismology and earthquake damage mitigation”, Annual Review of Earth and Planetary Sciences, Vol. 33, pp. 195–214, 2005. https://doi.org/10.1146/annurev.earth.33.092203.122626
S. Pavlides, A. Ganas, G. Papathanassiou, S. Valkaniotis, E. Thomaidou, G. Georgiadis, S. Sboras, and A. Chatzipetros, “Geological-seismotectonic study of the wider area of Ioannina (seismic region of the earthquake October 15, 2016),” Technical Report, 2016.
O. Mangira, E. Papadimitriou, and V. Karakostas, “Temporal variations of b-values in central Ionian Islands (Greece)”, Bulletin of Geophysics and Oceanography, Vol. 62, No. 1, pp. 19–32, 2021. https://doi.org/10.4430/bgta0341
R. Gaire et al., “Internet of Things (IoT) and Cloud Computing Enabled Disaster Management”, arXiv preprint arXiv:1806.07530, 2018. https://doi.org/10.48550/arXiv.1806.07530
A. Aljumah, A. Kaur, M. Bhatia, T. A. Ahanger “Internet of things fog computing based framework for smart disaster management”, Transactions on Emerging Telecommunications Technologies, Vol. 32, No. 8, art. no. e4078, 2021. https://doi.org/10.1002/ett.4078
C. Duverger , S. Lambotte , P. Bernard , H. Lyon-Caen , A Deschamps , A Nercessian “Dynamics of microseismicity and its relationship with the active structures in the western Corinth Rift (Greece)”, Geophysical Journal International, Vol. 215, No. 1, pp. 196–221, 2018. https://doi.org/10.1093/gji/ggy264
G. Chouliaras, “Investigating the earthquake catalog of the national observatory of Athens”, Natural Hazards and Earth System Sciences, Vol. 9, No. 3, pp. 905–912, 2009. https://doi.org/10.5194/nhess-9-905-2009
European-Mediterranean Seismological Centre (EMSC), “FDSN Web Services Specification Version 1.1”, Standard Report, pp. 1–45, 2019. [Online]. Available: https://www.fdsn.org/webservices/
W. McKinney, “Data structures for statistical computing in Python”, SciPy, 2010. https://doi.org/10.25080/Majora-92bf1922-00a
A. M. Dziewonski, T. A. Chou, and J. H. Woodhouse, “Determination of earthquake source parameters from waveform data for studies of global and regional seismicity,” Journal of Geophysical Research: Solid Earth, Vol. 86, No. B4, pp. 2825–2852, 1981. https://doi.org/10.1029/JB086iB04p02825
F. Pedregosa et al., “Scikit-learn: Machine learning in Python”, Journal of Machine Learning Research, Vol. 12, pp. 2825–2830, 2011. [Cross Ref]
M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise”, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, pp. 226–231, 1996. [Cross Ref]
M. Bath “B. Gutenberg and C. F. Richter, Seismicity of the Earth and Associated Phenomena”, Tellus, Vol. 2, No. 1, 1950. https://doi.org/10.3402/tellusa.v2i1.8523
P. Virtanen et al., “SciPy 1.0: Fundamental algorithms for scientific computing in Python,” Nature Methods, Vol. 17, pp. 261–272, 2020. https://doi.org/10.1038/s41592-019-0686-2
C. R. Harris et al., “Array programming with NumPy,” Nature, Vol. 585, pp. 357–362, 2020. https://doi.org/10.1038/s41586-020-2649-2
T. Utsu, “Representation and analysis of the earthquake size distribution: A historical review and some new approaches”, Seismicity Patterns, their Statistical Significance and Physical Meaning, pp. 509–535, 1999. http://doi.org/10.1007/978-3-0348-8677-2_15
Y. Ogata, “Statistical models for earthquake occurrences and residual analysis for point processes”, Journal of the American Statistical Association, Vol. 83, No. 401, pp. 9–27, 1988. https://doi.org/10.1080/01621459.1988.10478560
S. Wiemer, “A software package to analyze seismicity: ZMAP”, Seismological Research Letters, Vol. 72, No. 3, pp. 373–382, 2001. https://doi.org/10.1785/gssrl.72.3.373
J. D. Hunter, “Matplotlib: A 2D Graphics Environment”, Computing in Science & Engineering, Vol. 9, No. 3, pp. 90-95, May-June 2007. https://doi.org/10.1109/MCSE.2007.55
Plotly Technologies Inc., “Collaborative data science”, Technical Report, Montreal, QC, Canada, 2015. Available online: https://plot.ly
Y. Zhang, C. Zhan, Q. Huang, and D. Sornette, “Forecasting future earthquakes with deep neural networks: Application to California”, Geophysical Journal International, Vol. 240, No. 1, pp. 81–95, 2025. https://doi.org/10.1093/gji/ggae373
M. Han, L. Mizrahi, and S. Wiemer, “Towards a harmonized operational earthquake forecasting model for Europe”, Natural Hazards and Earth System Sciences, Vol. 25, No. 3, pp. 991–1012, 2025. https://doi.org/10.5194/nhess-25-991-2025
L. Mizrahi et al., “Developing, testing, and communicating earthquake forecasts: Current practices and future directions,” Reviews of Geophysics, Vol. 62, No. 3, art. no. e2023RG000823, 2024. https://doi.org/10.1029/2023RG000823
B. M. Asayesh, S. Hainzl, and G. Zöller, “Improved aftershock forecasts using mainshock information in the framework of the ETAS model”, Journal of Geophysical Research: Solid Earth, Vol. 130, No. 2, art. no. e2024JB030287, 2025. https://doi.org/10.1029/2024JB030287
O. Nicolis, L. Delgado, B. Peralta, M. Díaz, and M. Chiodi, “Space-time clustering of seismic events in Chile using ST-DBSCAN-EV algorithm”, Environmental and Ecological Statistics, Vol. 31, No. 2, pp. 509-536, 2024. https://doi.org/10.1007/s10651-023-00594-3
H. Hardianti, and R. Hidayat, “Implementation of DBSCAN for earthquake clustering in Indonesia with potential surface damage”, Journal of Mathematics, Computations and Statistics, Vol. 8, No. 1, pp. 124-133, 2025. https://doi.org/10.35580/jmathcos.v8i1.7318
N. V. Sarlis, E. S. Skordas, P. A. Varotsos, T. Nagao, M. Kamogawa, & S. Uyeda, “Spatiotemporal variations of seismicity before major earthquakes in the Japanese area and their relation with the epicentral locations”, Proceedings of the National Academy of Sciences, Vol. 112, No. 4, pp. 986-989, 2014. https://doi.org/10.1073/pnas.1422893112
E. Piegari, G. Camanni, M. Mercurio, and W. Marzocchi, “Illuminating the hierarchical segmentation of faults through an unsupervised learning approach applied to clouds of earthquake hypocenters”, Earth and Space Science, Vol. 11, No. 10, art. no. e2023EA003267, 2024. https://doi.org/10.1029/2023EA003267
S. Sharma, R. K. Vijay, and S. J. Nanda, “Identification and spatio temporal analysis of earthquake clusters using SOM–DBSCAN”, Neural Computing and Applications, Vol. 35, pp. 8081-8108, 2024. https://doi.org/10.1007/s00521-022-08085-5
L. Mizrahi, S. Nandan, B. M. Cabrera, and S. Wiemer, “suiETAS: Developing and testing ETAS based earthquake forecasting models for Switzerland,” Bulletin of the Seismological Society of America, Vol. 114, No. 5, pp. 2591–2612, 2024. https://doi.org/10.1785/0120240007
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