Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning
| العنوان: | Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning |
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| المؤلفون: | Gwantae Kim, Hanseok Ko, Bonhwa Ku |
| المصدر: | IEEE Geoscience and Remote Sensing Letters. 18:974-978 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Artificial neural network, Computer science, business.industry, Deep learning, Feature extraction, 0211 other engineering and technologies, 02 engineering and technology, Overfitting, Geotechnical Engineering and Engineering Geology, computer.software_genre, Domain (software engineering), Seismic analysis, Feature (machine learning), Artificial intelligence, Data mining, Electrical and Electronic Engineering, Transfer of learning, business, computer, 021101 geological & geomatics engineering |
| الوصف: | This letter proposes a multifeature fusion model using deep convolution neural networks and transfer learning approach for earthquake event classification. There are several feature representations for seismic analysis, such as the time domain, the frequency domain, and the time–frequency domain. To successfully classify various earthquake events, we propose a novel model that combines these features hierarchically. In addition, we apply a transfer learning to mitigate overfitting problem of deep learning model while achieving high classification performance. To evaluate our approach, we conduct experiments with the Korean peninsula earthquake database from 2016 to 2018 and a large earthquake database on the Circum-Pacific belt in 2019. The experimental results show that the proposed method outperforms over the compared state-of-the-art methods. |
| تدمد: | 1558-0571 1545-598X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::5498d6d97722f0d2f8893becc6292355 https://doi.org/10.1109/lgrs.2020.2993302 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........5498d6d97722f0d2f8893becc6292355 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580571 1545598X |
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