Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals
This study proposes a novel deep learning approach for predicting significant earthquakes (Mw <inline-formula> <tex-math notation="LaTeX">$\ge 5.0$ </tex-math></inline-formula>) in Turkey using ionospheric Total Electron Content (TEC) data and space weather indices....
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| Format: | Article |
| Language: | English |
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11053753/ |
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| author | Hakan Uyanik Mehmet Kokum Erman Senturk Mohamed Freeshah Salih T. A. Ozcelik Muhammed Halil Akpinar Serenay Celik Abdulkadir Sengur |
| author_facet | Hakan Uyanik Mehmet Kokum Erman Senturk Mohamed Freeshah Salih T. A. Ozcelik Muhammed Halil Akpinar Serenay Celik Abdulkadir Sengur |
| author_sort | Hakan Uyanik |
| collection | DOAJ |
| description | This study proposes a novel deep learning approach for predicting significant earthquakes (Mw <inline-formula> <tex-math notation="LaTeX">$\ge 5.0$ </tex-math></inline-formula>) 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. |
| format | Article |
| id | doaj-art-663cb68e76994431bd60656e9ce0f9b4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-663cb68e76994431bd60656e9ce0f9b42025-08-20T03:49:46ZengIEEEIEEE Access2169-35362025-01-011311620011621010.1109/ACCESS.2025.358374911053753Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric SignalsHakan Uyanik0https://orcid.org/0000-0002-6870-7569Mehmet Kokum1https://orcid.org/0000-0001-5149-3931Erman Senturk2Mohamed Freeshah3https://orcid.org/0000-0003-3539-7450Salih T. A. Ozcelik4https://orcid.org/0000-0002-7929-7542Muhammed Halil Akpinar5https://orcid.org/0000-0001-7563-0937Serenay Celik6Abdulkadir Sengur7https://orcid.org/0000-0003-1614-2639Electrical-Electronics Engineering Department, Engineering Faculty, Munzur University, Tunceli, TürkiyeGeological Engineering Department, Engineering Faculty, Fırat University, Elâzğ, TürkiyeDepartment of Geomatics, Kocaeli University, İzmit, TürkiyeCivil and Environmental Engineering Department, College of Engineering, UAE University, Al Ain, United Arab EmiratesElectrical-Electronics Engineering Department, Engineering Faculty, Bingol University, Bingöl, TürkiyeDepartment of Electronics and Automation, Vocational School of Technical Sciences, Istanbul University-Cerrahpaşa, Istanbul, TürkiyeElectrical-Electronics Engineering Department, Technology Faculty, Fırat University, Elâzğ, TürkiyeElectrical-Electronics Engineering Department, Technology Faculty, Fırat University, Elâzğ, TürkiyeThis study proposes a novel deep learning approach for predicting significant earthquakes (Mw <inline-formula> <tex-math notation="LaTeX">$\ge 5.0$ </tex-math></inline-formula>) 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.https://ieeexplore.ieee.org/document/11053753/TEC signalsEarthquake precursorspace-weather indicesmulti-input deep networksConvMixers |
| spellingShingle | Hakan Uyanik Mehmet Kokum Erman Senturk Mohamed Freeshah Salih T. A. Ozcelik Muhammed Halil Akpinar Serenay Celik Abdulkadir Sengur Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals IEEE Access TEC signals Earthquake precursor space-weather indices multi-input deep networks ConvMixers |
| title | Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals |
| title_full | Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals |
| title_fullStr | Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals |
| title_full_unstemmed | Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals |
| title_short | Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals |
| title_sort | seismic foresight a novel multi input 1d convolutional mixer model for earthquake prediction using ionospheric signals |
| topic | TEC signals Earthquake precursor space-weather indices multi-input deep networks ConvMixers |
| url | https://ieeexplore.ieee.org/document/11053753/ |
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