Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. Although constraining epicenters in an automated fashion is an established technique, constraining event depths is substantially more difficult. One solution to this challenge is teleseismic depth phases, but these can currently not be identified precisely by automatic detection methods. Here, we propose two deep‐learning models, DepthPhaseTEAM and DepthPhaseNet, to detect and pick depth phases. For training the models, we create a dataset based on the ISC‐EHB bulletin—a high‐quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axis yields accurate estimates of earthquake depth. Furthermore, we show how a multistation model, DepthPhaseTEAM, leads to better and more consistent predictions than the single‐station model, DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library.

中文翻译：

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学习深层和浅层：基于深度学习的深度相位拾取和地震深度估计

自动远震地震监测是全球地震活动分析的重要组成部分。尽管以自动化方式限制震中是一项成熟的技术，但限制事件深度要困难得多。应对这一挑战的一种解决方案是远震深度相位，但目前无法通过自动检测方法精确识别这些相位。在这里，我们提出了两种深度学习模型，DepthPhaseTEAM 和 DepthPhaseNet，来检测和选择深度相位。为了训练模型，我们创建了一个基于 ISC-EHB 公告的数据集——一个带有详细阶段注释的高质量目录。我们展示了如何将预测的相位到达概率曲线反投影到深度轴上来准确估计地震深度。此外，我们还展示了多站模型 DepthPhaseTEAM 如何比单站模型 DepthPhaseNet 带来更好、更一致的预测。为了允许直接应用我们的模型，我们将它们集成到 SeisBench 库中。