Earthquake prediction modeling using dynamic changes (Case Study: Alborz Region)

Document Type : Original Article


Department of Civil Engineering, Research Institute of ShakhesPajouh, Isfahan, Iran


This study aims to investigate the computational effect of Earth's viscosity on the Coulomb stress changes. Therefore, several large earthquakes in the Alborz region are selected and Coulomb stress changes are calculated in them, then the Coulomb stress temporal changes are shown by assuming the earth as an elastic layer on a viscous- elastic half-space. The spatial and temporal changes of the crustal deformation process associated with earthquakes depend on several parameters including the thickness of the lithosphere, viscosity of the asthenosphere, and dip angle of fault. The findings of this study are presented by determining the impact of modeling results on each of the input parameters through the sensitivity analysis of co-seismic and post-seismic deformation due to the dip-slip and strike-slip faulting. In addition to the useful results reported for the impact of parameters, the obtained results indicate the occurrence of numerous aftershocks in a region with increased Coulomb stress from 0.1 to 0.8 bar and the non-occurrence or low-occurrence of aftershocks in a region with reduced Coulomb stress. In addition to the predicted locations of aftershocks, it is also possible to determine the location of the next major earthquake using Coulomb stress change calculations.


1.     Sarlis, N. V., Skordas, E. S., and Varotsos, P. A., "Natural Time Analysis: Results Related to Two Earthquakes in Greece during 2019", Proceedings, Vol. 24, No. 1, (2019), 20. doi:10.3390/iecg2019-06194
2.     Sharma, S., Venkateswarlu, H., and Hegde, A., "Application of Machine Learning Techniques for Predicting the Dynamic Response of Geogrid Reinforced Foundation Beds", Geotechnical and Geological Engineering, Vol. 37, No. 6, (2019), 4845–4864. doi:10.1007/s10706-019-00945-7
3.     Sivandi-Pour, A., and Noroozinejad Farsangi, E., "Statistical Prediction of Probable Seismic Hazard Zonation of Iran Using Self-organized Artificial Intelligence Model", International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 4, (2019), 467–473. doi:10.5829/ije.2019.32.04a.02
4.     Sharma, N., Chakrabarti, A., Balas, V. E., and Martinovic, J., .Data Management, Analytics and Innovation, Vol. 1175, (2021) Singapore, Springer Singapore. doi:10.1007/978-981-15-5619-7
5.     Çakir, Z., Barka, A. A., and Evren, E., "Coulomb Stress Interactions and the 1999 Marmara Earthquakes", Turkish Journal of Earth Sciences, Vol. 12, No. 1, (2003), 91–103
6.     Ozturk, B. M., "Seismic drift response of building structures in seismically active and near -fault regions", Ph.D. Dissertations, Purdue University, (2003).
7.     King, G. C., Stein, R. S., and Lin, J., "Some characteristic features of the Anatolian fault zone", Bulletin of the Seismological Society of America, Vol. 84, No. 3, (1994), 935–953.
8.     Freed, A. M., and Lin, J., "Delayed triggering of the 1999 Hector Mine earthquake by viscoelastic stress transfer", Nature, Vol. 411, No. 6834, (2001), 180–183. doi:10.1038/35075548
9.     Wang, H., Liu, J., Shi, Y. L., Zhang, H., and Zhang, G. M., "Dynamic simulation of interactions between major earthquakes on the Xianshuihe fault zone", Science in China, Series D: Earth Sciences, Vol. 51, No. 10, (2008), 1388–1400. doi:10.1007/s11430-008-0110-8
10.   Paolucci, R., Mazzieri, I., Piunno, G., Smerzini, C., Vanini, M., and Özcebe, A. G., "Earthquake ground motion modeling of induced seismicity in the Groningen gas field", Earthquake Engineering & Structural Dynamics, Vol. 50, No. 1, (2021), 135–154. doi:10.1002/eqe.3367
11.   Al-Najjar, H. A. H., Kalantar, B., Pradhan, B., and Saeidi, V., "Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms",  Earth Resources and Environmental Remote Sensing/GIS Applications X, Vol. 11156, (2019), 19. doi:10.1117/12.2532687
12.   Galkina, A., and Grafeeva, N., "Machine learning methods for earthquake prediction: a Survey: A survey", Proceedings of the FourthConference on Software Engineering and Information Management (SEIM 2019), (2019), 25–32, RWTH Aahen University, 25–32
13.   Zhou, Z., Lin, Y., Zhang, Z., Wu, Y., and Johnson, P., "Earthquake detection in 1D time-series data with feature selection and dictionary learning", Seismological Research Letters, Vol. 90, No. 2 A, (2019), 563–572. doi:10.1785/0220180315
14.   Vasti, M., and Dev, A., "Classification and analysis of real-world earthquake data using various machine learning algorithms", Lecture Notes in Electrical Engineering, Vol. 612, (2020), 1–14. doi:10.1007/978-981-15-0372-6_1
15.   Hossain, M. S., Al Hasan, A., Guha, S., and Andersson, K., "A belief rule based expert system to predict earthquake under uncertainty", Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol. 9, No. 2, (2018), 26–41. doi:10.22667/JOWUA.2018.06.30.026
16.   Rundle, J. B., "Viscoelastic-gravitational deformation by a rectangular thrust fault in a layered Earth", Journal of Geophysical Research, Vol. 87, No. B9, (1982), 7787. doi:10.1029/JB087iB09p07787
17.   Smith, B., and Sandwell, D., "Coulomb stress accumulation along the San Andreas Fault system", Journal of Geophysical Research: Solid Earth, Vol. 108, No. B6, (2003). doi:10.1029/2002jb002136
18.   Thatcher, W., Matsuda, T., Kato, T., and Rundle, J. B., "Lithospheric loading by the 1896 Riku-u earthquake, northern Japan: implications for plate flexure and asthenospheric rheology.", Journal of Geophysical Research, Vol. 85, No. B11, (1980), 6429–6435. doi:10.1029/JB085iB11p06429
19.   Deng, J., and Sykes, L. R., "Evolution of the stress field in southern California and triggering of moderate-size earthquakes: A 200-year perspective", Journal of Geophysical Research: Solid Earth, Vol. 102, No. B5, (1997), 9859–9886. doi:10.1029/96jb03897
20.   Stein, S., and Wysession, M., An Introduction to Seismology, Earthquakes, and Earth Structure, (2009) John Wiley & Sons.
21.   Okada, Y., "Internal deformation due to shear and tensile faults in a half-space", Bulletin of the Seismological Society of America, Vol. 82, No. 2, (1992), 1018–1040.
22.   Gitis, V. G., and Derendyaev, A. B., "Web-based GIS platform for automatic prediction of earthquakes", Lecture Notes in
        Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10962 LNCS, (2018), 268–283. doi:10.1007/978-3-319-95168-3_18
23.   Asim, K. M., Idris, A., Iqbal, T., and Martínez-Álvarez, F., "Earthquake prediction model using support vector regressor and hybrid neural networks", PLoS ONE, Vol. 13, No. 7, (2018), 1–22. doi:10.1371/journal.pone.0199004
24.   Zhang, L., Si, L., Yang, H., Hu, Y., and Qiu, J., "Precursory Pattern Based Feature Extraction Techniques for Earthquake Prediction", IEEE Access, Vol. 7, (2019), 30991–31001. doi:10.1109/ACCESS.2019.2902224
25.   Fernández, J., Yu, T. T., and Rundle, J. B., "Horizontal viscoelastic-gravitational displacement due to a rectangular dipping thrust fault in a layered Earth model", Journal of Geophysical Research B: Solid Earth, Vol. 101, No. 6, (1996), 13581–13594. doi:10.1029/96jb00525
26.   Thatcher, W., and Rundle, J. B., "A viscoelastic coupling model for the cyclic deformation due to periodically repeated earthquakes at subduction zones.", Journal of Geophysical Research, Vol. 89, No. B9, (1984), 7631–7640. doi:10.1029/JB089iB09p07631
27.   Wells, D. L., and Coppersmith, K. J., "New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement", Bulletin of the Seismological Society of America, Vol. 84, No. 4, (1994), 974–1002
28.   Okada, Y., "Surface deformation due to shear and tensile faults in a half-space", Bulletin of the Seismological Society of America, Vol. 75, No. 4, (1985), 1135–1154
29.   Mignan, A., "A preliminary text classification of the precursory accelerating seismicity corpus: inference on some theoretical trends in earthquake predictability research from 1988 to 2018", Journal of Seismology, Vol. 23, No. 4, (2019), 771–785. doi:10.1007/s10950-019-09833-2
30.   Razifard, M., Shoaei, G., and Zare, M., "Application of fuzzy logic in the preparation of hazard maps of landslides triggered by the twin Ahar-Varzeghan earthquakes (2012)", Bulletin of Engineering Geology and the Environment, Vol. 78, No. 1, (2019), 223–245. doi:10.1007/s10064-018-1235-4
31.   Pandit, A., and Biswal, K. C., "Prediction of earthquake magnitude using adaptive neuro fuzzy inference system", Earth Science Informatics, Vol. 12, No. 4, (2019), 513–524. doi:10.1007/s12145-019-00397-w
32.   Asim, K. M., Idris, A., Martinez-Alvarez, F., and Iqbal, T., "Short Term Earthquake Prediction in Hindukush Region Using Tree Based Ensemble Learning", Proceedings - 14th International Conference on Frontiers of Information Technology, FIT 2016, (2017), 365–370. doi:10.1109/FIT.2016.073