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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning
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Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning

机译:基于融合的融合的地震事件分类使用转移学习

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摘要

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.
机译:这封信提出了一种利用深卷积神经网络和地震事件分类的转移学习方法的多因素融合模型。 存在若干特征表示用于地震分析,例如时域,频域和时频域。 要成功分类各种地震事件,我们提出了一种新颖的模型,可以层次地结合这些功能。 此外,我们申请转让学习来缓解深层学习模型的过度拟合问题,同时实现高分类性能。 为了评估我们的方法,我们在2016年到2018年与朝鲜半岛地震数据库进行了实验,并在2019年在循环带上进行了大地震数据库。实验结果表明,所提出的方法优于比较的比较状态 艺术方法。

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