Artificial neural networks to model earthquake magnitude and the di-rection of its energy propagation: a case study of Indonesia

نوع مقاله : مقاله پژوهشی‌

نویسندگان

1 Assistant Professor, Department of Physics, Faculty of Sciences and Engineering, University of Nusa Cendana, Kupang, Nusa Tenggara Timur, Indonesia

2 Assistant Professor, Polytechnic of Indonesian Technical School of Textiles Bandung, Indonesia

3 Associate Professor, Department of Physics, Faculty of Sciences and Engineering, University of Nusa Cendana, Kupang, Nusa Tenggara Timur, Indonesia

چکیده

Indonesia is a region that experiences frequent earthquakes, and therefore is highly prone to earthquake hazards. Elevated seismicity in Indonesia means that building models to understand and predict earthquake characteristics and their associated hazards is important. The goal of this research is to develop a supervised learning artificial neural network (ANN) application that can predict the magnitude of earthquake wave propagation (bar) and the direction of propagation of earthquake wave using some selected earthquake data (Mw 5-8) happened in Indonesia from 1996 to 2019. The data was taken from the United States Geological Survey (USGS) database and the Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) database. The earthquake data used in the artificial neural network application consists of a hidden layer with four neurons and seven input neurons that contain earthquake parameters, including longitude, latitude, magnitude, depth, strike, dip, and rake, as well as one output neuron. The magnitude and direction of energy propagation of the earthquakes were successfully predicted using the ANN program. Excellent agreement between the results of ANN and those of Coulomb 3.3 software strongly indicates that the ANN program can be used as an alternative to the existing Coulomb 3.3 software. The ANN model can also be further applied to other earthquake data around the world. The results are expected to contribute to the development of earthquake detection software tools. With artificial intelligence, earthquake prediction software will be more effective and can reduce the risks of failure in predicting the magnitude and direction of earthquake wave propagation.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Artificial neural networks to model earthquake magnitude and the di-rection of its energy propagation: a case study of Indonesia

نویسندگان [English]

  • Hery Leo Sianturi 1
  • Valentinus Galih Vidia Putra 2
  • Redi Kristian Pingak 3
1 Assistant Professor, Department of Physics, Faculty of Sciences and Engineering, University of Nusa Cendana, Kupang, Nusa Tenggara Timur, Indonesia
2 Assistant Professor, Polytechnic of Indonesian Technical School of Textiles Bandung, Indonesia
3 Associate Professor, Department of Physics, Faculty of Sciences and Engineering, University of Nusa Cendana, Kupang, Nusa Tenggara Timur, Indonesia
چکیده [English]

Indonesia is a region that experiences frequent earthquakes, and therefore is highly prone to earthquake hazards. Elevated seismicity in Indonesia means that building models to understand and predict earthquake characteristics and their associated hazards is important. The goal of this research is to develop a supervised learning artificial neural network (ANN) application that can predict the magnitude of earthquake wave propagation (bar) and the direction of propagation of earthquake wave using some selected earthquake data (Mw 5-8) happened in Indonesia from 1996 to 2019. The data was taken from the United States Geological Survey (USGS) database and the Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) database. The earthquake data used in the artificial neural network application consists of a hidden layer with four neurons and seven input neurons that contain earthquake parameters, including longitude, latitude, magnitude, depth, strike, dip, and rake, as well as one output neuron. The magnitude and direction of energy propagation of the earthquakes were successfully predicted using the ANN program. Excellent agreement between the results of ANN and those of Coulomb 3.3 software strongly indicates that the ANN program can be used as an alternative to the existing Coulomb 3.3 software. The ANN model can also be further applied to other earthquake data around the world. The results are expected to contribute to the development of earthquake detection software tools. With artificial intelligence, earthquake prediction software will be more effective and can reduce the risks of failure in predicting the magnitude and direction of earthquake wave propagation.

کلیدواژه‌ها [English]

  • Earthquake
  • magnitude
  • energy propagation
  • artificial neural network
  • Coulomb stress

مراجع

Alarifi, A. S. N., Alarifi, N. S. N., and Al-Humidan, S., 2012, Earthquakes magnitude predication using artificial neural network in northern Red Sea area: Journal of King Saud University- Science, 24, 301-313.
Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., and Pour, A. B., 2018, A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment: Remote Sensing, 10(6), 975.
Asim, K. M., Idris, A., Iqbal, T., and Martínez-Álvarez, F., 2018, Earthquake prediction model using support vector regressor and hybrid neural networks: PLoS ONE, 13(7), e0199004.
Bradley, K. E., Feng, L., Hill, E. M., Natawidjaja, D. H., and Sieh, K., 2017, Implications of the diffuse deformation of the Indian Ocean lithosphere for slip partitioning of oblique plate convergence in Sumatra: Journal of Geophysical Research: Solid Earth, 122, 572–591.
Brooks, H., and Tucker, N., 2015, Electrospinning predictions using artificial neural networks: Polymer, 58(1), 22–29.
Faridi-Majidi, R., Ziyadi, H., Naderi, N., and Amani, A., 2012, Use of artificial neural networks to determine parameters controlling the nanofibers diameter in electrospinning of nylon-6, 6: Journal of Applied Polymer Science, 124(2), 1589–1597.
Huang, Z., Argyroudis, S. A., Pitilakis, K., Zhang, D., and Tsinidis, G., 2022, Fragility assessment of tunnels in soft soils using artificial neural networks: Underground Space, 7(2), 242-253.
Hutchings, S. J., and Mooney, W. D., 2021, The seismicity of Indonesia and tectonic implications: Geochemistry, Geophysics, Geosystems, 22, e2021GC009812.
King, G. C. P., Stein, R. S., and Lin, J., 1994, Static stress changes and the triggering of earthquakes: Bulletin of the Seismological Society of America, 84(3), 935-953.
Liu, Z. Y. C., and Harris, R. A., 2014, Discovery of possible mega-thrust earthquake along the Seram trough from records of 1629 tsunami in eastern Indonesian region: Natural Hazards, 72, 1311–1328.
Miao, M., and Shou-Biao, Z., 2012, Study of the impact of static Coulomb stress changes of megathrust earthquakes along subduction zone on the following aftershocks: Chinese Journal of Geophysics, 55(5), 539-551.
Mignan, A., and Broccardo, M., 2020, Neural network applications in earthquake prediction (1994–2019): Meta‐analytic and statistical insights on their limitations: Seismological Research Letters, 91(4), 2330–2342.
Moustra, M., Avraamides, M., and Christodoulou, C., 2011, Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals: Experts Systems with Applications, 38(12), 15032-15039.
Naghibzadeh, M., and Adabi, M., 2014, Evaluation of effective electrospinning parameters controlling gelatin nanofibers diameter via modelling artificial neural networks: Fibers and Polymers, 15(4), 767–777.
Narayanakumar, S., and Raja, K., 2016, A BP artificial neural network model for earthquake magnitude prediction in Himalayas, India: Circuits and Systems, 7, 3456-3468.
Nasouri, K., Shoushtari, A. M., and Khamforoush, M., 2013, Comparison between artificial neural network and response surface methodology in the prediction of the production rate of polyacrylonitrile electrospun nanofibers: Fibers and Polymers, 14(11), 1849–1856.
Nugraha, A. M. S., and Hall, R., 2018, Late Cenozoic paleogeography of Sulawesi, Indonesia: Palaeogeography, Palaeoclimatology, Palaeoecology, 490, 191–209.
Okada, Y., 1992, Internal deformation due to shear and tensile faults in a halfspace: Bulletin of the Seismology Society of America, 82, 1018–1040.
Oktarina, R., Bahagia, S. N., Diawati, L., and Pribadi, K. S., 2019, Artificial neural network for predicting earthquake casualties and damages in Indonesia: IOP Conference Series: Earth and Environmental Science, 426, 012156.
Panakkat, A., and Adeli, H., 2007, Neural network models for earthquake magnitude prediction using multiple seismicity indicators: International Journal of Neural Systems, 17(1), 13-33.
Parsons, T., Robert, S. Y., Yuji, Y., and Ahmad, H., 2006, Static stress change from the 8 October, 2005 M = 7.6 Kashmir earthquake: Geophysical Research Letters, 33(6).
Patria, A., and Putra, P. S., 2020, Development of the Palu-Koro fault in NW Palu Valley, Indonesia: Geoscience Letters, 7, 1–11.
Pozos-Estrada, A., and Gómez, R., 2014, Use of neural network to predict the peak ground accelerations and pseudo spectral accelerations for Mexican Inslab and Interplate Earthquakes: Geofísica Internacional, 53(1), 39-57.
Sahara, D. P., Nugraha, A. D., Muhari, A., et al., 2021, Source mechanism and triggered large aftershocks of the Mw 6.5 Ambon, Indonesia earthquake: Tectonophysics, 799, 228709.
Shodiq, M. N., Kusuma, D. H., Rifqi, M. G., Barakbah, A. R., and Harsono, T., 2018, Neural network for earthquake prediction based on automatic clustering in Indonesia: International Journal on Informatics Visualization, 2(1), 37-43.
Sianturi, H. L., Mohamad, J. N., Mala, H. U., and Tanesib, J. L., 2018, The identification of stress Coulomb changes in Lombok earthquakes on August 5 and 9, 2018: Proceeding the first international conference and exhibition on sciences technology, ICEST, University of Nusa Cendana, Labuan Bajo, Indonesia, 174-183.
Socquet, A., Hollingsworth, J., Pathier, E., and Bouchon, M., 2019, Evidence of supershear during the 2018 magnitude 7.5 Palu earthquake from space geodesy: Nature Geoscience, 12, 192–199.
Suhardja, S. K., Widiyantoro, S., Métaxian, J. P., Rawlinson, N., Ramdhan, M., and Budi-Santoso, A., 2020, Crustal thickness beneath Mt. Merapi and Mt. Merbabu, Central Java, Indonesia, inferred from receiver function analysis: Physics of the Earth and Planetary Interiors, 302, 106455.
Supendi, P., Nugraha, A. D., Puspito, N. T., Widiyantoro, S., and Daryono, D., 2018, Identification of active faults in West Java, Indonesia, based on earthquake hypocenter determination, relocation, and focal mechanism analysis: Geoscience Letters, 5, 31.
Syifa, M., Ryoo, S., and Lee, C. W., 2019, Post-earthquake damage mapping using artificial neural network and support vector machine classifiers at Palu, Indonesia: IGARSS 2019- 2019 IEEE International Geoscience and Remote Sensing Symposium, 9577-9580.
Toda, S., Stein, R. S., Sevilgen, V., and Lin, J., 2011, Coulomb 3.3 graphic-rich deformation and stress-change software for earthquake, tectonic, and volcano research and teaching—user guide: U.S. Geological Survey Open-File Report 2011-1060, 63 p., available at http://pubs.usgs.gov/of/2011/1060/.
Widiyantoro, S., Gunawan, E., Muhari, A., Rawlinson, N., Mori, J., and Hanifa, N. R., 2020, Implications for megathrust earthquakes and tsunamis from seismic gaps south of Java Indonesia: Nature: Scientific Reports, 10, 15274.
 
مجله ژئوفیزیک ایران
دوره 17، شماره 6
اسفند 1402
صفحه 9-23

فایل ها

هم رسانی

ارجاع به این مقاله

آمار