LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers

AI 中文解读

LithoFormer来了！这个全新AI框架给地质勘探装上了“透视眼”，一口气读完整口井的测井数据，就能精准画出地下岩层分布，把地层分界误差一口气砍掉90%。以前分析地下岩层就像盲人摸象，只能一小段一小段看，经常把不同地层接错，就像拼图丢了关键几块。现在这个AI模型运用了类似ChatGPT的序列处理能力，同时兼顾岩层不能颠倒的物理规则，直接消灭了地层错乱的老大难问题。这项技术不仅让地质工程师减少了80%的人工校核工作量，更重要的是，它能帮我们更准确地找到适合封存二氧化碳的地层、开发地热资源，甚至优化石油天然气开采。对普通人来说，这意味着更高效的碳减排和更清洁的能源开发，同时也让地下工程更安全可靠——毕竟地层搞错了，打井可是要出大事的。

arXiv:2607.22804v1 Announce Type: new Abstract: Accurate geological characterization of subsurface reservoirs from well log data is essential to support projects such as carbon capture and storage (CCS), geothermal development, and extraction of natural resources. Existing automated techniques for geological characterization primarily use sliding-window classification, which limits their ability to understand broader geological contexts, often leading to misaligned formation layers. To overcome these limitations, we introduce LithoFormer, a robust framework for stratigraphic inference using a Seq2Seq transformer model that ingests entire multivariate well logs in a single pass. The framework utilizes a channel-independent PatchTST backbone enhanced with rotary positional embeddings (RoPE) to capture long-range geological dependencies across entire multivariate well logs. A decoupled multi-task head is employed to jointly predict geological zonation and precise boundary probabilities, while a geology-informed loss function enforces physical constraints such as the Law of Superposition. Validated and deployed on three real-world datasets, LithoFormer demonstrates a 90% reduction in median boundary error and eliminates stratigraphic order violations compared to traditional sliding-window baselines. It also achieves a 80% reduction in manual expert labor and eliminates stratigraphic inconsistencies, providing a scalable and reliable solution for large-scale subsurface modeling.