Evaluating Multi‐Agent and Wavelet‐Transform Uncertainties in Lunar Seismic Ambient Noise Exploration
Year: 2026
Type: article
Abstract: Passive seismic ambient noise interferometry (ANI) has shown potential for lunar seismic exploration, offering the capability to detect near‐surface subsurface structures critical for future lunar mission, such as near‐surface ice deposits and lava tubes, without the need for active seismic sources. Performing ANI on the Moon can be realized with a multi‐agent system, in which a network of individual rovers either carry or deploy seismic receivers. However, these systems have inherent uncertainties in localization and timing. Additionally, methods used to extract dispersion curves from cross‐correlations are fundamentally limited in achievable time–frequency resolution, which we demonstrate for the continuous wavelet transform (CWT). Quantifying how these factors propagate into Rayleigh wave velocity estimates is essential for accurate detection of lunar subsurface features. In this study, analytical error formulas are derived and validated through Monte Carlo simulations using passive seismic data from the Apollo 17 lunar seismic profiling experiment. Results indicate that velocity uncertainties due to localization errors remain around an acceptable for realistic positional standard deviations of at the receiver distance of as in the Apollo 17 Lunar Seismic Profiling Experiment. Timing errors induced by clock instabilities are negligible. However, uncertainties in seismic travel‐time estimations are significantly dominated by the resolution limits imposed by the CWT. The developed analytical uncertainty model thus provides a critical foundation for designing autonomous lunar seismic networks for future lunar missions.
Source: Earth and Space Science
DOI: 10.1029/2025ea004631
Language: English
Topic: Seismic Waves and Analysis
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,650
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W7130836930
