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- MakaleAçık Erişim2025Scopus SJRQ1Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals
Uyanik, Hakan; Kokum, Mehmet; Senturk, Erman; Freeshah, Mohamed; Ozcelik, Salih T. A.; Halil Akpinar, Muhammed; Celik, Serenay; Sengur, Abdulkadir
This study proposes a novel deep learning approach for predicting significant earthquakes (Mw ≥ 5.0 ) in Turkey using ionospheric Total Electron Content (TEC) data and space weather indices. ConvMixer is a lightweight CNN architecture that blends spatial and channel information using depthwise convolutions and pointwise layers. Inspired by vision transformers, it offers efficient image classification with fewer parameters and high performance. We developed a multi-input one-dimensional convolutional mixer (MI-1D-ConvMixer) model to classify TEC data from the preceding five consecutive days as either precursory to an earthquake on the 6th day or normal. The model incorporates six inputs: five 1D TEC signals and one 1D space-weather index array, including the global geomagnetic index (Kp), storm duration distribution (Dst), sunspot number (R), geomagnetic storm index (Ap-index), solar wind speed (Vsw), and solar activity index (F10.7) are also utilized to reveal non-seismic related pre-earthquake ionospheric variations. Our methodology involves two stages: 1) a preprocessing stage to enhance TEC signals and 2) an end-to-end training of the MI-1D-ConvMixer model. The model architecture features depth-wise and point-wise convolutions with patch embedding, utilizing uses four tunable hyperparameters: network depth, hidden dimension size, kernel size, and patch size. We used 196 earthquakes data from Turkey from 2010-2023, and TEC data from the TNPGN-Active GNSS stations. The dataset was split into 75% for training and 25% for testing. Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. This research contributes to the field by introducing a novel deep learning architecture specifically designed for integrating TEC and space weather data for earthquake prediction. Future work should focus on validating the model’s performance in different geographical regions and investigating its limitations.
AtıfScopus7OpenAlex5
İncele - MakaleAçık Erişim2023Scopus SJRQ1A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content
Uyanık, Hakan; Şentürk, Erman; Akpınar, Muhammed Halil; Ozcelik, Salih T. A.; Kokum, Mehmet; Freeshah, Mohamed; Sengur, Abdulkadir
Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large earthquakes (e.g., the ionospheric Total Electron Content (TEC)). The TEC is an important parameter used to detect pre-earthquake changes by measuring ionospheric disturbances and space weather indices, such as the global geomagnetic index (Kp), the storm duration distribution (Dst), the sunspot number (R), the geomagnetic storm index (Ap-index), the solar wind speed (Vsw), and the solar activity index (F10.7), have also been used to detect pre-earthquake ionospheric changes. In this study, the feasibility of the 6th-day earthquake prediction by the deep neural network technique using the previous five consecutive days is investigated. For this purpose, a two-staged approach is developed. In the first stage, various preprocessing steps, namely TEC signal improvement and time-frequency representation-based TEC image construction, are performed. In the second stage, a multi-input convolutional neural network (CNN) model is designed and trained in an end-to-end fashion. This multi-input CNN model has a total of six inputs, and five of the inputs are designed as 2D and the sixth is a 1D vector. The 2D inputs to the multi-input CNN model are TEC images and the vector input is concatenated space weather indices. The network branches with the 2D inputs contain convolution, batch normalization, and Rectified Linear Unit (ReLU) activation layers, and the branch with the 1D input contains a ReLU activation layer. The ReLU activation outputs of all the branches are flattened and then concatenated. And the classification is performed via fully connected, softmax, and classification layers, respectively. In the experimental work, earthquakes with a magnitude of Mw5.0 and above that occurred in Turkey between 2012 and 2019 are used as the dataset. The TEC data were recorded by the Turkey National Permanent GNSS Network-Active (TNPGN-Active) Global Navigation Satellite System (GNSS) stations. The TEC data five days before the earthquake were marked as “precursor days” and the TEC data five days after the earthquake were marked as “normal days”. In total, 75% of the dataset is used to train the proposed method and 25% of the dataset is used for testing. The classification accuracy, sensitivity, specificity, and F1-score values are obtained for performance evaluations. The results are promising, and an 89.31% classification accuracy is obtained.
AtıfScopus27OpenAlex19İncele