用集成模型与丢弃层提升地震波形分类的不确定性估计能力
Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
- 构建多个不同设置的卷积神经网络并集成，增强预测可靠性
- 集成模型结合丢弃层后，不确定性估计效果显著提升
- 丢弃层有效缓解误标数据影响，提升模型鲁棒性
深度学习在多个领域取得显著成功，包括地震学。然而，深度学习面临误标样本和模型不确定性估计两大挑战。本研究利用卷积神经网络（CNN）基于初动极性对地震波形进行分类，训练了多种设置的CNN模型，并构建网络集成以估计不确定性。结果表明，各训练设置均表现良好，集成方法在不确定性估计方面优于单个网络。进一步发现，引入丢弃层可显著增强集成模型的不确定性估计能力。此外，不同训练设置的对比显示，使用丢弃层能提升网络对误标样本的鲁棒性。
原文摘要 · Abstract (English)
Deep learning models have demonstrated remarkable success in various fields, including seismology. However, one major challenge in deep learning is the presence of mislabeled examples. Additionally, accurately estimating model uncertainty is another challenge in machine learning. In this study, we develop Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. We trained multiple CNN models with different settings. We also constructed ensembles of networks to estimate uncertainty. The results showed that each training setting achieved satisfactory performances, with the ensemble method outperforming individual networks in uncertainty estimation. We observe that the uncertainty estimation ability of the ensembles of networks can be enhanced using dropout layers. In addition, comparisons among different training settings revealed that the use of dropout improved the robustness of networks to mislabeled examples.
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