Seismic Interferometry for Detecting Ice-Bearing Rocks on the Moon

SEISMIC INTERFEROMETRY FOR DETECTING ICE-BEARING ROCKS ON THE MOON. S. Keil1 ([email protected]e), R. Sesko2, A. Prieto Lazaro2, H. Igel1, P. Reiss2, 1Ludwig-Maximilians-Universität München, 2Technical University of Munich Introduction: There are numerous upcoming space missions with the objective of exploring the lunar surface and ultimately returning humans to the Moon. This includes building long-term infrastructure both in orbit and on the lunar surface. Hereby, the use of locally available resources, such as water ice, is of scientific, economic, and exploratory importance. Water ice can be utilized for astronauts, and oxygen and hydrogen can be used to produce rocket fuel [1][2]. However, the viability of using water ice as a resource depends on its concentration and accessibility. Water ice on the Moon is primarily located in the permanently shadowed regions (PSR), where conditions are favorable for its preservation. Orbital instruments have detected surface ice with concentrations ranging from several hundred ppm to several percent by mass at varying depths [3]. Deeper ice deposits, potentially meters to tens of meters thick, have been inferred from crater measurements and impact gardening models, indicating that significant ice may be buried beyond 5–10 meters [4][5]. Since the different remote sensing instruments have detection limits, uncertainties remain regarding the exact location of ice-bearing rocks, particularly at depth. To address this challenge, alternative strategies are needed to accurately map the distribution, thickness, depth, and concentration of lunar ice deposits. Seismic methods offer a promising solution, as even small amounts of ice can significantly affect seismic velocities in the regolith [6]. In particular, passive seismic methods are advantageous as no active signal is required. Instead, naturally occurring seismic signals can be used. Studies have shown that thermal moonquakes generate an almost continuous signal [7] and thus are suitable as a source for passive seismic methods. Sepcifically, the method of seismic interferometry has proven to be effective for subsurface imaging on the Moon [8][9]. In this study we define target locations for the search of ice-bearing rocks on the Moon and test the method of seismic interferometry for mapping such structures. The method is applied to synthetic data generated from numerical simulations with a 2D digital twin of the Moon’s shallow subsurface structure. Methodology: An analysis of potential target locations for deployment of seismic stations is performed on the basis of existing remote-sensing derived datasets. These deliver an understanding of the expected properties of the ice-bearing subsurface and preliminary operational considerations for a robotic mission to the south pole. The candidate locations at the south pole are graded based on their area, estimated ice stability by [10], geological unit, and operational boundary conditions (illumination [11], slope, and roughness [12]) for a rover mission with the current state of the art. The method of seismic interferometry is based on the cross-correlation of recordings acquired at two seismic stations. The main assumption hereby is that the cross-correlation yields the empirical Green’s function (GF) between the receivers [13]. From these GFs surface wave dispersion curves can be extracted, which are then inverted to determine the local subsurface velocity structure. To test seismic interferometry for ice detection, synthetic data for the Moon are generated using numerical simulations. The seismic simulations are conducted in 2D using the spectral element code SALVUS [14]. The subsurface model is adapted from [9] who constructed the shallow part of the underlying velocity model based on seismic interferometry results from passive Apollo 17 data, and the deep structure from previous studies that analyzed the active seismic data of Apollo 17. To replicate the scattering characteristics of the regolith layer, velocity fluctuations were introduced into the homogeneous layered velocity model using a von Karman autocorrelation function. A receiver line is implemented on the surface with an inter-station spacing of 5 m, spanning a total distance of 400 m. To mimic thermal moonquake activity, sources are randomly distributed on the surface to the left and right of the receiver line with frequencies ranging from 3 to 20 Hz. To determine the resolution limits for detecting ice-bearing rocks, a heterogeneity with a simple block-like geometry is implemented beneath the receiver line. Key parameters, including the width, depth, thickness, and velocity contrast of the heterogeneity, are varied. To relate P-wave and S-wave velocities to the mass fraction of ice in the regolith, we use the relationship established by [6]. The complete simulation setup is shown in Fig. 1. Figure 1: Schematic setup of the 2D numerical simulation model. Results: Target Locations: The expected presence of icebearing regolith is constrained to the very polar regions of lunar surface in the partially or permanent shadowed regions. Deploying large arrays of sesmic sensors in PSRs, either statically or on rovers, would be technicallally challenging. The chosen locations therefore lie in only partially shadowed regions, with good accessibility for current rover technology especially regarding terrain and solar illumination for power generation. Ice-Detection: The effect of the ice-bearing regolith on the seismic interferometry results is evident in the GF observed in the correlations (Fig.2). A noticeable travel-time difference in the Rayleigh wave peak appears when comparing dry and ice-bearing regolith. The presence of ice increases wave velocity, resulting in a shorter GF lag time, which clearly indicates a subsurface heterogeneity between the two stations. Figure 2: GF of dry regolith compared to a medium containing a 50 m wide ice body with 2wt.% ice. Rayleigh wave group velocity dispersion curves are extracted from the GF results to examine the impact of ice-bearing rock on absolute velocity. Even small weight percentages of ice cause a noticeable difference compared to dry regolith (Fig. 3a). However, due to the inherent nature of group velocity, the increase in velocity does not follow a linear trend with ice content. The width of the ice body (Fig. 3b) also plays a significant role—small ice bodies (<10 m) Figure 3: Group velocity dispersion curves extracted from correlations for ice bodies with varying a) wt.% ice and b) width. are indistinguishable from dry regolith, while larger ice bodies result in a clear increase in seismic velocity. In addition, the depth of the ice body influences detectability. Our results show that in the frequency range of thermal moonquakes (> 3 Hz), surface wave sensitivity is limited to the upper 10 m. Furthermore, the scattering of the lunar regolith mainly influences interferometry results for large inter-station distances. Conclusions: Our findings demonstrate that seismic interferometry is well-suited for imaging icebearing rocks, as even small amounts of ice cause a notable increase in seismic velocities. However, the method has limitations for very small or deep icebodies. This is not a major drawback, as evidence suggests that water ice may be stable even at very shallow depths in permanently shadowed regions. Acknowledgements: This work was supported by the research project NEPOS (Project num.: IG 16/21-1) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). References: [1] Kornuta, D. et al. (2019) Reach, 13, 100026. [2] Anand, M. (2012) Planetary and Space Science, 74(1), 42-48. [3] Reiss, P. (2024) Proc. Natl. Acad. Sci. 121 (52). [4] Cannon, K. M. et al. (2020) GRL 47(21). [5] Costello, E. S. et al. (2020) Journal of Geophysical Research: Planets 125(3). [6] Kunimasu, T. et al. (2023) Icarus 406 [7] Duennebier, F. & Sutton, G. H. (1974) Journal of Geophysical Research 79(29). [8] Tanimoto, T. et al. (2008) Journal of Geophysical Research: Planets 113(E8) [9] Keil, S. et al. (2024) Earth and Space Science 11(10) [10] Schorghofer, N., & Williams, J. P. (2020) The Planetary Science Journal, 1(3), 54. 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