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

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Title: Acceleration‐Related Glitch Patterns in Apollo Seismic Data and Implications for Future Lunar Seismic Observation.
Authors: Liu, Xin1,2 (AUTHOR), Xiao, Zhuowei1,2 (AUTHOR), Li, Juan1,2,3 (AUTHOR) juanli@mail.iggcas.ac.cn
Source: Journal of Geophysical Research. Planets. Oct2025, Vol. 130 Issue 10, p1-21. 21p.
Subject Terms: *Seasonal temperature variations, Sunrise & sunset, Moon, Measurement uncertainty (Statistics), Space flight to the moon, Sensor placement, Seismic surveys, Deep learning
Company/Entity: Apollo program (U.S.)
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. Plain Language Summary: The Apollo seismic data greatly advanced our understanding of the Moon's internal structure. However, the harsh lunar environment also produced numerous brief, transient, and often repetitive signals, generally referred to as "glitches." These glitches offer valuable lessons for the design and deployment of future lunar seismic experiments, yet they remain incompletely understood. In this study, we used a combination of deep learning and template matching to identify and catalog acceleration‐related glitches in the Apollo seismic data. We discovered that glitches often occur around lunar sunrise and sunset due to rapid surface temperature changes. Aside from the lunar sunrise and sunset, additional glitches are linked to moving shadows from nearby objects or lunar eclipses, while glitch activity during lunar night is significantly lower. We also observed a group of glitches with elliptical polarization, differing from the linearly polarized ones seen in Martian data. Furthermore, the number and type of glitches vary by station, showing that instrument placement and environmental conditions strongly affect data quality. Our results contribute to the understanding of Apollo seismic data and provide a guide for optimizing seismometer deployment and observation schedules in future lunar seismic missions. Key Points: We compiled acceleration‐related glitch catalogs from Apollo seismic data from 1969 to 1977 via deep clustering and matrix profileGlitch patterns mainly relate to drastic temperature shifts triggered by lunar sunrise, sunset, nearby shadows, and even lunar eclipsesProvides valuable references for optimizing observation timing and deployment strategies in future lunar seismic observations [ABSTRACT FROM AUTHOR]
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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. Plain Language Summary: The Apollo seismic data greatly advanced our understanding of the Moon's internal structure. However, the harsh lunar environment also produced numerous brief, transient, and often repetitive signals, generally referred to as "glitches." These glitches offer valuable lessons for the design and deployment of future lunar seismic experiments, yet they remain incompletely understood. In this study, we used a combination of deep learning and template matching to identify and catalog acceleration‐related glitches in the Apollo seismic data. We discovered that glitches often occur around lunar sunrise and sunset due to rapid surface temperature changes. Aside from the lunar sunrise and sunset, additional glitches are linked to moving shadows from nearby objects or lunar eclipses, while glitch activity during lunar night is significantly lower. We also observed a group of glitches with elliptical polarization, differing from the linearly polarized ones seen in Martian data. Furthermore, the number and type of glitches vary by station, showing that instrument placement and environmental conditions strongly affect data quality. Our results contribute to the understanding of Apollo seismic data and provide a guide for optimizing seismometer deployment and observation schedules in future lunar seismic missions. Key Points: We compiled acceleration‐related glitch catalogs from Apollo seismic data from 1969 to 1977 via deep clustering and matrix profileGlitch patterns mainly relate to drastic temperature shifts triggered by lunar sunrise, sunset, nearby shadows, and even lunar eclipsesProvides valuable references for optimizing observation timing and deployment strategies in future lunar seismic observations [ABSTRACT FROM AUTHOR]
ISSN:21699097
DOI:10.1029/2025JE009277