Accurate P-wave first-motion-polarity (FMP) information can contribute to solving earthquake focal mechanisms, especially for small earthquakes, to which waveform-based methods are generally inapplicable due to the computationally expensive high-frequency waveform simulations and inaccurate velocity models. In this paper, we propose a deep-learning-based method for the automatic determination of the FMPs, named “DiTingMotion”. DiTingMotion was trained with the P-wave FMP labels from the “DiTing” and SCSN-FMP datasets, and it achieved ∼97.8% accuracy on both datasets. The model maintains ∼83% accuracy on data labeled as “Emergent”, of which the FMP labels are challenging to identify for seismic analysts. Integrated with HASH, we developed a workflow for automated focal mechanism inversion using the FMPs identified by DiTingMotion and applied it to the 2019 M 6.4 Ridgecrest earthquake sequence for performance evaluation. In this case, DiTingMotion yields comparable focal mechanism results to that using manually determined FMPs by SCSN on the same data. The results proved that the DiTingMotion has a good generalization ability and broad application prospect in rapid earthquake focal mechanism inversion.

中文翻译：

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DiTingMotion：一种深度学习的第一运动极性分类器及其在震源机制反演中的应用

准确的 P 波初动极性 (FMP) 信息有助于解决地震震源机制，特别是对于小地震，由于计算量大的高频波形模拟和不准确的速度模型，基于波形的方法通常不适用于小地震。在本文中，我们提出了一种基于深度学习的 FMP 自动确定方法，名为“DiTingMotion”。DiTingMotion 使用来自“DiTing”和 SCSN-FMP 数据集的 P 波 FMP 标签进行训练，并且在两个数据集上都达到了～97.8% 的准确率。该模型对标记为“紧急”的数据保持 ~83% 的准确度，其中 FMP 标签对于地震分析人员来说具有挑战性。结合HASH，我们使用 DiTingMotion 识别的 FMP 开发了自动震源机制反演工作流程，并将其应用于 2019 M 6.4 Ridgecrest 地震序列以进行性能评估。在这种情况下，DiTingMotion 产生的焦点机制结果与 SCSN 在相同数据上使用手动确定的 FMP 产生的结果相当。结果证明DiTingMotion在地震震源机制快速反演中具有良好的泛化能力和广阔的应用前景.