The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean‐teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source‐type classification using unlabeled data.

中文翻译：

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半监督学习在地震监测中的应用

深层神经网络在一系列地震监测任务中表现出的出色性能很大程度上取决于事件目录的可用性，这些事件目录已经经过多年或数十年的手动管理。但是，地震事件目录的质量，持续时间和可用性在监视操作，区域和目标的范围内差异很大。半监督学习（SSL）可以从标记和未标记的数据中进行学习，并提供了一个框架，可利用大量未经审查的地震数据来针对各种目标任务训练深度神经网络。我们将两种SSL算法（均值教师和虚拟对抗训练）以及一种新颖的混合技术（指数平均对抗训练）应用于地震事件分类，以研究采用SSL的未标记数据如何增强模型性能。通常，我们发现SSL可以用更少的标签执行和监督学习。在某些情况下，我们还观察到SSL的几乎一半好处是通过SSL技术实施的有意义的正则化的结果，并且可能不会直接归因于未标记的数据。最后，当我们评估使用未标记的数据来表征新地理区域中的来源时，未标记的数据的好处在于可预测任务的难度。在受监督的模型性能较低的地理区域中，