عنوان مقاله :
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
پديد آورندگان :
Akhoondzadeh ، Mehdi University of Tehran - School of Surveying and Geospatial Engineering, College of Engineering - Department of Photogrammetry and Remote Sensing , Hosseiny ، Benyamin University of Tehran - School of Surveying and Geospatial Engineering, College of Engineering - Department of Photogrammetry and Remote Sensing , Ghasemian ، Nafise University of Tehran - School of Surveying and Geospatial Engineering, College of Engineering - Department of Photogrammetry and Remote Sensing
كليدواژه :
Earthquake Precursor , anomaly , Ionosphere , GPS , TEC , neural network
چكيده فارسي :
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 Sarpole Zahab, Iran. Six different Neural Network (NN) algorithms including MultiLayer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), LongShort Term Memory (LSTM) and CNNLSTM 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 solargeomagnetic conditions, every six methods detect anomalous TEC variations nine days prior to the earthquake. Since timeseries 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 timeseries. Moreover, multimethods analyses beside the multi precursor’s analyses decrease uncertainty and false alarms and consequently lead to confident anomalies.
عنوان نشريه :
فيزيك زمين و فضا
عنوان نشريه :
فيزيك زمين و فضا
