基于1200口井数据的地质基础模型，实现多任务跨井解释，精度显著提升。
WLFM: A Well-Logs Foundation Model for Multi-Task and Cross-Well Geological Interpretation
- 将测井曲线分块编码为地质词条，通过自监督预训练与层位感知对比学习建模。
- 在孔隙度估计上达0.0041 MSE，岩性分类准确率74.13%，微调后分别提升至0.0038和78.10%。
- 具备可复用的地质词汇表与层位感知能力，适合地质AI多模态融合场景。
测井解释是地下表征的基础，但受工具响应异质性、信号噪声和标签稀缺挑战。我们提出WLFM，一个在1200口井的多曲线测井数据上预训练的基础模型，包含三个阶段：将测井片段分块为地质词条，通过掩码词建模与地层感知对比学习进行自监督预训练，并采用少样本微调进行多任务适配。WLFM在多个基准上持续优于现有方法，孔隙度估计的均方误差达0.0041，岩性分类准确率为74.13%；经微调后进一步提升至0.0038和78.10%。除预测精度外，WLFM展现出涌现的层位感知能力，学习到可复用的地质词汇表，并能以合理保真度重构被遮蔽的曲线，但在浅层和超深层区间存在系统性偏差。尽管未显式评估边界检测，聚类分析表明其具备未来扩展潜力。这些结果确立了WLFM作为可扩展、可解释且可迁移的地质人工智能骨干模型，对测井、地震与文本数据的多模态融合具有重要意义。
原文摘要 · Abstract (English)
Well-log interpretation is fundamental for subsurface characterization but remains challenged by heterogeneous tool responses, noisy signals, and limited labels. We propose WLFM, a foundation model pretrained on multi-curve logs from 1200 wells, comprising three stages: tokenization of log patches into geological tokens, self-supervised pretraining with masked-token modeling and stratigraphy-aware contrastive learning, and multi-task adaptation with few-shot fine-tuning. WLFM consistently outperforms state-of-the-art baselines, achieving 0.0041 MSE in porosity estimation and 74.13\% accuracy in lithology classification, while WLFM-Finetune further improves to 0.0038 MSE and 78.10\% accuracy. Beyond predictive accuracy, WLFM exhibits emergent layer-awareness, learns a reusable geological vocabulary, and reconstructs masked curves with reasonable fidelity, though systematic offsets are observed in shallow and ultra-deep intervals. Although boundary detection is not explicitly evaluated here, clustering analyses suggest strong potential for future extension. These results establish WLFM as a scalable, interpretable, and transferable backbone for geological AI, with implications for multi-modal integration of logs, seismic, and textual data.
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