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MLP, Recurrent, Convolutional and LSTM Neural Networks Detect Seismo-TEC Anomalies Potentially Related to the Iran Sarpol-e Zahab (Mw=7.3) Earthquake of 12 November 2017 | ||
| فیزیک زمین و فضا | ||
| مقاله 7، دوره 47، شماره 4، بهمن 1400، صفحه 111-124 اصل مقاله (4.01 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2021.326054.1007332 | ||
| نویسندگان | ||
| Mehdi Akhoondzadeh* 1؛ Benyamin Hosseiny2؛ Nafise Ghasemian2 | ||
| 1Associate Professor, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Ph.D. Student, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| A strong earthquake () (34.911° N, 45.959° E, ~19 km depth) occurred on November 12, 2017, at 18:18:17 UTC (LT=UTC+03:30) in Sarpol-e Zahab, Iran. Six different Neural Network (NN) algorithms including Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and CNN-LSTM were implemented to survey the four months of GPS Total Electron Content (TEC) measurements during the period of August 01 to November 30, 2017 around the epicenter of the mentioned earthquake. By considering the quiet solar-geomagnetic conditions, every six methods detect anomalous TEC variations nine days prior to the earthquake. Since time-series of TEC variations follow a nonlinear and complex behavior, intelligent algorithms such as NN can be considered as an appropriate tool for modelling and prediction of TEC time-series. Moreover, multi-methods analyses beside the multi precursor’s analyses decrease uncertainty and false alarms and consequently lead to confident anomalies. | ||
| کلیدواژه‌ها | ||
| Earthquake Precursor؛ Anomaly؛ Ionosphere؛ GPS-TEC؛ Neural Network | ||
| عنوان مقاله [English] | ||
| MLP, Recurrent, Convolutional and LSTM Neural Networks Detect Seismo-TEC Anomalies Potentially Related to the Iran Sarpol-e Zahab (Mw=7.3) Earthquake of 12 November 2017 | ||
| نویسندگان [English] | ||
| Mehdi Akhoondzadeh1؛ Benyamin Hosseiny2؛ Nafise Ghasemian2 | ||
| 1Associate Professor, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Ph.D. Student, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| A strong earthquake () (34.911° N, 45.959° E, ~19 km depth) occurred on November 12, 2017, at 18:18:17 UTC (LT=UTC+03:30) in Sarpol-e Zahab, Iran. Six different Neural Network (NN) algorithms including Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and CNN-LSTM were implemented to survey the four months of GPS Total Electron Content (TEC) measurements during the period of August 01 to November 30, 2017 around the epicenter of the mentioned earthquake. By considering the quiet solar-geomagnetic conditions, every six methods detect anomalous TEC variations nine days prior to the earthquake. Since time-series of TEC variations follow a nonlinear and complex behavior, intelligent algorithms such as NN can be considered as an appropriate tool for modelling and prediction of TEC time-series. Moreover, multi-methods analyses beside the multi precursor’s analyses decrease uncertainty and false alarms and consequently lead to confident anomalies. | ||
| کلیدواژه‌ها [English] | ||
| Earthquake Precursor, Anomaly, Ionosphere, GPS-TEC, Neural Network | ||
| مراجع | ||
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Akhoondzadeh, M., Parrot, M. and Saradjian, M. R., 2010a, Electron and ion density variations before strong earthquakes (M>6.0) using DEMETER and GPS data. Nat. Hazards Earth Syst. Sci., 10, 7– 18. doi: 10.5194/nhess-10-7-2010. Akhoondzadeh, M., 2012, Anomalous TEC variations associated with the powerful Tohoku earthquake of 11 March 2011. Nat. Hazards Earth Syst. Sci., 12, 1453-1462, doi:10.5194/nhess-12-1453-2012. Akhoondzadeh, M., 2013a, A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies. Advances in Space Research, 51, 2048-2057. Akhoondzadeh, M., De Santis, A., Marchetti, D., Piscini, A. and Cianchini, G., 2018, Multi precursors analysis associated with the powerful Ecuador (MW=7.8) earthquake of 16 April 2016 using Swarm satellites data in conjunction with other multi-platform satellite and ground data. Advances in Space Research, 61, 248-263. https://doi.org/10.1016/j.asr.2017.07.014. Akhoondzadeh, M., De Santis, A., Marchetti, D., Piscini, A. and Jin, S., 2019, Anomalous seismo-LAI variations potentially associated with the 2017 Mw = 7.3 Sarpol-e Zahab (Iran) earthquake from Swarm satellites, GPS-TEC and climatological data. Advances in Space Research, 64, 143–158. https://doi.org/10.1016/j.asr.2019.03.020 Chung, J., Gulcehre, C., Cho, K. and Bengio, Y., 2014, Empirical evaluation of gated recurrent neural networks on sequence modeling. arxiv prepr. arxiv1412.3555. Freund, F., 2009, Stress-activated positive hole charge carriers in rocks and the generation of pre-earthquake signals. in electromagnetic phenomena associated with earthquakes, ed. by m. Hayakawa, Transworld research network, Trivandrum, 41-96. Goodfellow, I., Bengio, Y. and Courville, A., 2016, Deep learning, Mit press. Graves, A., 2012, In supervised sequence labelling with recurrent neural networks. springer, berlin, Heidelberg, 5-12. Graves, A., 2013, Generating sequences with recurrent neural networks. Arxiv Prepr. Arxiv1308.0850. Hayakawa, M. and Molchanov, O. A., 2002, Seismo- electromagnetics: lithosphere-atmosphere-ionosphere coupling. terra scientific publishing co. Tokyo, 477. Huang, C. J. and Kuo, P. H., 2018, A deep CNN-LSTM model for particulate matter (pm2. 5) forecasting in smart cities. Sensors, 18 (7), 2220-2232. Khan, S., Rahmani, H., Shah, S.A.A. and Bennamoun, M., 2018, A guide to convolutional neural networks for computer vision. Synthesis Lectures on Computer Vision, 8(1), 1-207. Liu, J.Y., Chuo, Y.J., Shan, S.J., Tsai, Y.B., Pulinets, S.A. and Yu, S.B., 2004, Pre-earthquake-ionospheric anomalies registered by continuous GPS TEC. Ann. Geophys., 22, 1585-1593. Mannucci, A. J., Wilson, B. D., Yuan, D. N., Ho, C. H., Lindqwister, U. J. and Runge, T. F., 1998, A global mapping technique for GPS-derived ionospheric total electron content measurements, Radio Sci., 33, 565-582, doi: 10.1029/97RS02707. Parrot, M., 1995, Use of satellites to detect seismo-electromagnetic effects, main phenomenological features of ionospheric precursors of strong earthquakes. Advances in Space Research, 15 (11), 1337-1347. Pulinets, S. and Boyarchuk, K. A., 2004, Ionospheric precursors of earthquakes. Springer, berlin. Pulinets, S. and Ouzounov, D., 2011, Lithosphere - atmosphere - ionosphere coupling (LAIC) model - An unified concept for earthquake precursors validation. Journal of Asian Earth sciences, 41, 371-382. Sorokin, V. M. and Pokhotelov, O. A., 2014, Model for the vlf/lf radio signal anomalies formation associated with earthquakes. Advances in Space Research, 54 (12), 2532-2539. Williams, R. J. and Zipser, D., 1989, A learning algorithm for continually running fully recurrent neural networks. Neural Comput., 1 (2), 270–280. | ||
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