Acceleration‐Related Glitch Patterns in Apollo Seismic Data and Implications for Future Lunar Seismic Observation

Abstract Apollo seismic data have significantly advanced our understanding of the Moon's internal structure and seismic activity. Beyond these scientific insights, the data also contain numerous glitches caused by the harsh lunar environment. Characterizing these glitches is crucial for improving future lunar seismic observations, which is particularly timely and important given upcoming lunar missions carrying new seismometer payloads. In this study, we combined deep learning and template matching to identify and catalog acceleration‐related glitches in the Apollo seismic data, revealing distinct temporal patterns correlated with lunar diurnal and seasonal cycles. Concentrations of glitches near lunar sunrise and sunset are likely caused by rapid temperature variations. Daytime glitches are associated with shadows cast by nearby objects or lunar eclipses. We also detect elliptically polarized glitches, differing from the linear polarization typical of Martian glitches and warranting further investigation. Our glitch catalogs reveal significantly fewer glitches during lunar nighttime compared to daytime, providing valuable insights for optimizing observation timing. Furthermore, variations in daytime glitch patterns across stations highlight the significant impact of station location and instrument deployment on data quality, demanding careful consideration in future lunar missions. In summary, this study compiles acceleration‐related glitch catalogs from Apollo seismic data, enhancing our understanding of the impacts of the lunar environment on seismic observations and providing valuable references for optimizing seismic observation strategies and instrument deployment in upcoming lunar missions.