标题： 地震相关联器的基准测试：合成场景中的见解 显示英文标题

标题： Benchmarking seismic phase associators: Insights from synthetic scenarios

摘要： 可靠的地震目录对地震学至关重要。 在相位拾取之后，相位关联将到时分组为具有一致起源的集合（即事件），确定事件数量，并识别异常拾取。 为了处理由于改进的拾取方法和更大规模部署而带来的地震相位拾取数量的显著增加，最近提出了一些新颖的相位关联器。 本研究对五种地震相位关联器进行了详细的基准分析，包括经典方法和基于机器学习的方法：PhaseLink、REAL、GaMMA、GENIE 和 PyOcto。 我们使用模拟数据集来模仿地壳和俯冲带场景中的真实地震特征。 我们在不同条件下评估性能，包括低噪声和高噪声环境、网络外事件、非常高的事件率以及可变的台站密度。 结果揭示了精度、召回率和计算效率方面的显著差异。 GENIE 和 PyOcto 表现出稳健的性能，在大多数场景中几乎达到完美表现。 只有在噪声水平和事件率最高的最困难条件下，性能有所下降，但仍保持 F1 分数高于 0.8。 PhaseLink 的性能随着噪声和事件密度的增加而下降，特别是在俯冲带中，最复杂的情况下几乎降至零。 GaMMA 比 PhaseLink 表现更好，但在高噪声、高密度场景中面临准确性和可扩展性的挑战。 REAL 表现合理，但在极端条件下失去召回率。 PyOcto 和 PhaseLink 在小规模数据集上表现出最快的运行时间，而 REAL 和 GENIE 在这些情况下要慢一个数量级以上。 在最高的拾取率下，GENIE 的运行时间劣势减少，与 PyOcto 相当，并且能够有效扩展。 我们的结果可以指导地震目录编制人员和新型关联器设计者。 显示英文摘要

摘要： Reliable seismicity catalogs are essential for seismology. Following phase picking, phase association groups arrivals into sets with consistent origins (i.e., events), determines event counts, and identifies outlier picks. To handle the substantial increase in the quantity of seismic phase picks from improved picking methods and larger deployments, several novel phase associators have recently been proposed. This study presents a detailed benchmark analysis of five seismic phase associators, including classical and machine learning-based approaches: PhaseLink, REAL, GaMMA, GENIE, and PyOcto. We use synthetic datasets mimicking real seismicity characteristics in crustal and subduction zone scenarios. We evaluate performance for different conditions, including low- and high-noise environments, out-of-network events, very high event rates, and variable station density. The results reveal notable differences in precision, recall, and computational efficiency. GENIE and PyOcto demonstrate robust performance, with almost perfect performance for most scenarios. Only for the most challenging conditions with high noise levels and event rates, performance drops but still maintains F1 scores above 0.8. PhaseLink's performance declines with noise and event density, particularly in subduction zones, dropping to near zero in the most complex cases. GaMMA outperforms PhaseLink but struggles with accuracy and scalability in high-noise, high-density scenarios. REAL performs reasonably but loses recall under extreme conditions. PyOcto and PhaseLink show the quickest runtimes for smaller-scale datasets, while REAL and GENIE are more than an order of magnitude slower for these cases. At the highest pick rates, GENIE's runtime disadvantage diminishes, matching PyOcto and scaling effectively. Our results can guide practitioners compiling seismicity catalogs and developers designing novel associators.