Verifiably grounded machine interpretation of lunar geology
Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations. Here, we investigate how far this interpretive workflow can be automated by a multimodal vision-language model. Focusing on the stratigraphy of lunar basaltic mare volcanism, we train a model to generate verifiably grounded geologic interpretations directly from co-registered topographic, spectral, and geologic maps. We demonstrate that while the system successfully balances established geological priors with local visual evidence to accurately describe stratigraphy and terrain, numeric age dating derived solely from vision defaults to memorized priors. Integrating an open-...
| Authors | Tom Sander, Kay Wohlfarth, Christian Wöhler |
|---|---|
| Primary category | cs.CL |
| Published | 10 August 2026 |
| Last updated at source | 20 August 2026 |
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