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
1 Ph.D. Student, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Ph.D. Student, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Associate Professor, Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
doi
10.22059/jesphys.2021.326054.1007332چکیده
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.
