发表机构
The University of Western Australia（西澳大利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对钻孔建模中自回归预测未被充分探索的问题，提出DrillBench基准，结合大规模预训练与空间检索，在保持局部性能的同时提升跨空间和跨省偏移下的泛化能力。
AI 中文摘要
自回归建模通过从先前观测中学习预测未来状态，在语言和序列任务中取得了显著成功。矿产勘探钻孔为这一范式提供了一个自然但尚未充分探索的场景：随着钻探的进行，岩性从浅层到深层依次揭示，使得对更深地层的预测天然具有自回归性质。然而，现有的钻孔建模主要被空间插值和重建方法所主导，或大量依赖掩码建模，严格的自回归预测在很大程度上未被充分探索。我们引入了DrillBench，这是一个包含49,671个西澳大利亚钻孔的基准，用于下一层预测和自回归地层生成，涵盖从局部预测到空间偏移再到跨地质省迁移的分级迁移谱系。对经典模型、地质统计模型和神经模型的基准测试揭示了一个清晰的“迁移边界”：空间和地球化学条件化在局部提供了显著增益，但在更强偏移下急剧恶化，而岩性序列自回归模型则迁移得更为稳健。基于这一发现，我们开发了一种与骨干网络无关的方法，将历史钻孔的大规模预训练与相邻岩性的空间检索相结合。检索在风化覆盖层中最为有效，此时局部空间连续性仍具有信息量，而预训练在基岩和更广泛的地质偏移下贡献更为显著。两者结合，在保持强大局部性能的同时，改善了空间和跨省偏移下的泛化能力，尤其是在最遥远的划分上效果最为显著。基准和代码可在以下网址获取：此https URL。
英文摘要
Autoregressive modelling has achieved remarkable success in language and sequence tasks by learning to predict future states from previous observation. Mineral-exploration drillholes provide a natural but largely unexplored setting for this paradigm: as drilling proceeds, lithology is revealed sequentially from shallow to deep, making prediction of deeper strata inherently autoregressive. Existing drillhole modelling, however, is dominated by spatial interpolation and reconstruction, or largely rely on masked modelling, leaving strictly autoregressive prediction largely underexplored. We introduce DrillBench, a benchmark of 49,671 Western Australian drillholes for next-layer prediction and autoregressive stratigraphic generation across a graded transfer spectrum, from local prediction through spatial shift to cross geological province transfer. Benchmarking classical, geostatistical, and neural models reveals a clear \emph{transfer boundary}: spatial and geochemical conditioning provides large local gains but deteriorates sharply under stronger shift, whereas lithology-sequence autoregressive models transfer more robustly. Guided by this finding, we develop a backbone-agnostic recipe combining large-scale pretraining on historical drillholes with spatial retrieval of neighbouring lithology. Retrieval is most effective in weathered cover, when local spatial continuity remains informative, whereas pretraining contributes more strongly in bedrock and under broader geological shift. Together, they retain strong local performance while improving generalisation under spatial and cross-province shift, most markedly on the most distant splits. The benchmark and code are available at https://github.com/yihaoding/drillbench.
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