Estimating the critical angle of top near‐surface layers on the Moon

Abstract Near‐surface characterization of the lunar subsurface is essential for future exploration and infrastructure development, particularly for the construction of underground habitats that provide protection against radiation and micrometeorites. However, conventional seismic approaches for estimating subsurface properties typically rely on prior velocity models or multicomponent data, which are not available for legacy Apollo datasets. In this study, we propose a data‐driven approach for estimating the critical angle (CA) and phase rotation of P‐wave reflection events using active‐source seismic data from the Apollo 16 mission. The CA is directly related to subsurface velocity contrasts, making it a key parameter for constraining near‐surface structure. Our method is based on spectral recomposition, in which an inversion scheme is used to reconstruct the seismic spectrum of individual wavelets and extract their fundamental properties without requiring prior geological information. The proposed workflow is applied to near‐surface lunar seismic data, allowing the detection of the CA and estimation of phase variations along reflection events. The results show that the estimated CA values are consistent with previously reported velocity structures, with differences on the order of a few per cent. These findings indicate that the method can reliably characterize near‐surface lunar layers under limited data conditions. This work demonstrates the potential of data‐driven spectral methods for extracting physically meaningful parameters from sparse and degraded seismic datasets, providing a practical tool for future lunar geophysical investigations and site characterization.