AI 中文总结
研究针对地震震相关联难题，提出VORA方法，将其作为无监督时空聚类问题，依赖地震起源时间和台站邻接约束，利用深度学习估计起源时间，借助Voronoi图聚类，在合成与真实数据集上表现出色，能从局部扩展到全球，无需训练可推广。
AI 中文摘要
地震震相关联是迈向更完整可靠地震活动性目录的关键一步，因密集地震网络和先进震相拾取方法产生的大量震相数据集，使其成为一项具有挑战性的任务。本文提出VORA，一种高效且可扩展的地震震相关联器，将关联视为无监督时空聚类问题。它依赖地震起源时间估计和地震台站邻接两个主要约束条件。深度学习模型的进展使S波和P波检测效果相似，便于从候选震相对估计地震起源时间。利用Voronoi图定义台站邻居，对相邻台站的起源时间进行聚类，并应用可选的子聚类步骤分离重叠事件。合成和真实数据集的基准测试表明，VORA运行时间最快，即使在地震活动强烈时也能保持稳健性能。将其应用于覆盖2019年Ridgecrest地震序列的两周全球尺度拾取数据集，进一步证明该框架可从局部网络扩展到全球台站集。VORA无需训练，可在局部到全球区域、不同速度模型和不断演变的网络几何结构中推广，有助于满足不断扩展的地震网络和自动化震相拾取量增加的需求。
英文摘要
Earthquake phase association, which groups seismic phase arrivals into common origins, is a key step towards more complete and reliable seismicity catalogs. It has become a challenging task because of the massive phase datasets produced from dense seismic networks and advanced phase picking methods. Here we present VORA (Voronoi tessellation- and Origin-time-based Rapid Associator), an efficient and scalable earthquake phase associator that treats association as an unsupervised spatio-temporal clustering problem. Specifically, VORA depends on two primary constraints: the estimated earthquake origin time (temporal) and seismic station adjacency (spatial). Recent advances in deep learning models have enabled detection of S- and P-phases with similar effectiveness, making it straightforward to estimate corresponding earthquake origin times from candidate phase pairs given a prescribed range of velocity ratios or directly from raw waveforms. We then leverage the Voronoi diagram to define each station's neighbors, cluster the origin times across neighboring stations, and apply an optional sub-clustering step to separate overlapping events. Benchmarks on synthetic and real datasets show that VORA achieves the fastest runtime and maintains robust performance (high recall and precision) even under intense seismicity. Applying it to a two-week global-scale pick dataset covering the 2019 Ridgecrest earthquake sequence further demonstrates that the same framework scales from local networks to a global station set. VORA requires no training and generalizes across local-to-global regions, varying velocity models, and evolving network geometries, helping to meet the growing demands of expanding seismic networks and the increasing volume of automated phase picks.
