A new method of simultaneous time domain cross‐correlation of multiple components of motion at multiple seismic stations is developed for P‐ and S‐wave identification for determination of arrival times of deep moonquakes using Apollo Passive Seismic Experiment (APSE) data. Deep moonquakes occur in selenographically isolated clusters, allowing the stacking of a large number of moonquakes in each cluster to improve signal‐to‐noise ratio. The method developed here seeks to improve upon multichannel cross‐correlation (MCCC) of a single component of motion and multiple events used in past work by incorporating all components and stations simultaneously. This multicomponent multichannel cross‐correlation (MCMCCC) method not only maximizes the inherent correlation between the 12 components (three components of motion at each of four stations) across all events in a given cluster, but also establishes representative stacked traces of moonquakes within each cluster. Additionally, the MCMCCC method utilizes components and events with the highest data quality to guide the inclusion of lower quality components and events (or events with fewer components). A closure difference residual time is introduced to identify moonquake events with better data and/or close source locations, to further improve the signal‐to‐noise ratio of stacked traces. A multicomponent short‐term to long‐term average algorithm is also developed to objectively determine P‐ and S‐wave arrivals that best align with their travel time curves based on lunar velocity models, simultaneously for all 12 components of motion. Newly computed deep moonquake cluster stacks and some updated deep moonquake cluster locations are presented, which improve upon past results.

中文翻译：

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利用深月阿波罗记录进行数据叠加和P波和S波到达时间确定的新方法

为利用Apollo被动地震实验（APSE）数据确定深地震的到达时间，开发了一种在多个地震台站同时进行运动多个分量同时时域互相关的新方法，用于确定P波和S波。深月形地震发生在地形隔离的星团中，从而使每个星团中都堆积了大量的地震，从而提高了信噪比。此处开发的方法旨在通过同时合并所有组件和站来改进运动的单个组件和过去工作中使用的多个事件的多通道互相关（MCCC）。这种多分量多通道互相关（MCMCCC）方法不仅可以使给定星团中所有事件的12个分量（四个站中的每个运动的三个分量）之间的固有相关性最大化，而且可以在每个星团中建立代表性的堆叠地震痕迹。另外，MCMCCC方法利用具有最高数据质量的组件和事件来指导包含较低质量的组件和事件（或具有较少组件的事件）。引入了闭合差剩余时间来识别具有更好数据和/或靠近震源位置的地震事件，以进一步提高叠层迹线的信噪比。还开发了一种多分量短期到长期平均算法，可以同时针对所有12个运动分量，根据月球速度模型客观地确定最适合其行进时间曲线的P波和S波到达。提出了新计算出的深层地震星团堆栈和一些最新的深层地震星团位置，这些数据对过去的结果有所改进。