Optimal Design of Adaptive Seismic Networks for Lunar Seismic Exploration

Kurzfassung

The study investigates how adaptive seismic sensor networks can be optimized for lunar subsurface exploration, with particular emphasis on detecting near-surface water ice. Within the NEPOS project, multiple autonomous rovers are envisioned as mobile seismic stations that can reposition themselves during an exploration campaign. Rather than using a fixed sensor geometry, the proposed approach applies Bayesian optimal experimental design to identify measurement locations that are expected to provide the greatest information about subsurface properties. The optimization therefore combines scientific objectives, such as improving sensitivity to ice-bearing structures, with practical constraints arising from multi-agent operation and rover mobility. This enables the network configuration to be updated sequentially as new seismic measurements become available. The work builds on previous analyses of uncertainties in rover localization, timing, and seismic data processing, which affect the reliability of inferred subsurface properties. By integrating these uncertainties into the experimental-design framework, the approach aims to make lunar seismic surveys more efficient and informative. Adaptive rover networks could consequently reduce measurement effort while improving the characterization of scientifically and operationally relevant lunar resources.