Atmospheric Gravity Wave Detection Using Transfer Learning Techniques
Atmospheric Gravity Wave Detection Using Transfer Learning Techniques
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DOI：
10.1109/bdcat56447.2022.00023
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Jorge L. Gonzalez;Theodore Chapman;Kathryn Chen;Hannah M. Nguyen;Logan Chambers;S. A. Mostafa;
中科院分区：
文献类型：
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作者：
Jorge L. Gonzalez;Theodore Chapman;Kathryn Chen;Hannah M. Nguyen;Logan Chambers;S. A. Mostafa;
当重力试图通过大气中的稳定层恢复扰动时，就会产生大气重力波。它们对许多大气现象有明显的影响，如全球环流和空气湍流。然而，尽管它们很重要，但很少有人研究如何使用机器学习算法来检测重力波。我们在研究中面临两个主要挑战：我们的原始数据有很多噪音，标记的数据集非常小。在这项研究中，我们探索了各种预处理和迁移学习的方法，以应对这些挑战。我们在训练自动编码器对标记数据进行分类之前，对未标记数据进行了预训练。我们还通过将ImageNet上训练的InceptionV3模型中的某些预训练层与自定义层和自定义学习率调度器相结合，创建了一个自定义CNN。实验表明，我们的最佳模型在测试准确率方面比表现最好的基线模型高出6.36%。
Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy.