发表机构
TU Dortmund University（多特蒙德工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将地质知识推理方法嵌入多模态视觉-语言架构，构建可验证的月球地质学机器解释模型，结合开卷检索解决数值年龄定年依赖记忆先验的问题，明确自动地质推理的必要架构。
AI 中文摘要
行星地质学依赖历史解释性推理，从多样观测中重建过去事件。本文通过将地质知识发现与推理的独特方法嵌入多模态视觉-语言架构，向自动化“机器智能地质学家”迈出一步。聚焦月球玄武质月海火山作用的地层学，我们训练模型直接从配准的地形、光谱和地质地图生成可验证的地质解释。我们表明，该系统成功平衡既定地质先验与局部视觉证据，准确描述地层学和地形，但仅从视觉得出的数值年龄定年默认依赖记忆的先验。整合开卷检索机制可解决此问题，使模型能准确引用已发表的年表。我们的研究结果明确了自动地质推理所需的架构：站点证据必须从局部数据进行视觉解释，而定量历史背景必须从科学记录中检索。
英文摘要
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-book retrieval mechanism resolves this, enabling the model to faithfully cite published chronologies. Our findings delineate the necessary architecture for automated geologic inference: site evidence must be visually interpreted from local data, while quantitative historical context must be retrieved from the scientific record.
