Refined Characterization of Prevalent Short-Period (5-15 s) Signals in Glitch-Contaminated Apollo Seismic Data via A Deep Learning Workflow

Q: What does this research mean for the field?

Prevalent short-period (5-15 s) signals in Apollo seismic data are most likely caused by the thermal response of the Suprathermal Ion Detector rather than natural seismic activity. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Xin Liu; Zhuowei Xiao; Juan Li

doi:10.1016/j.asr.2026.05.084

May 1, 2026Advances in Space ResearchOpen Access

Refined Characterization of Prevalent Short-Period (5-15 s) Signals in Glitch-Contaminated Apollo Seismic Data via A Deep Learning Workflow

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Key Points

The aim is to refine the characterization of short-period signals in seismic data using a deep learning workflow.
Utilized a deep learning workflow to analyze glitch-contaminated Apollo seismic data.
Focused on short-period signals likely linked to the thermal response of the Suprathermal Ion Detector.
Proposed extensions of the methodology to Martian seismic data and other periodic signals.
Developed a refined characterization method for identifying short-period signals amidst glitches.
Indicated that the signals are predominantly associated with the thermal response mechanism.
Established potential applicability of the methodology to analyze seismic data from Mars.

Implication

Demonstrates refined characterization of short-period signals in seismic data, indicating broader implications for Martian research.

Abstract

• Deep-learning workflow refines short-period signals (SPS) characterization. • SPS are most likely related to the thermal response of Suprathermal Ion Detector. • The workflow can extend to Martian seismic data and other periodic signals.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a1d216202fbce9130637691 https://doi.org/10.1016/j.asr.2026.05.084

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Refined Characterization of Prevalent Short-Period (5-15 s) Signals in Glitch-Contaminated Apollo Seismic Data via A Deep Learning Workflow

Key Points

Implication

Abstract

Discussion

Cite This Study

Refined Characterization of Prevalent Short-Period (5-15 s) Signals in Glitch-Contaminated Apollo Seismic Data via A Deep Learning Workflow

Key Points

Implication

Abstract

Discussion

Cite This Study

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