LithoFormer：一种通过Transformer进行地层推断的稳健框架
LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers
- Laboratoire Interdisciplinaire des Sciences du Numérique, Université Paris-Saclay（巴黎萨克雷大学跨学科数字科学实验室）
- CentraleSupélec（中央理工-高等电力学院）
- CNRS（法国国家科学研究中心）
- SLB Montpellier（斯伦贝谢公司蒙彼利埃分公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对地下储层地质特征描述问题，LithoFormer利用Seq2Seq变压器模型，结合带旋转位置嵌入的PatchTST主干、解耦多任务头和地质信息损失函数，相比传统滑动窗口方法，减少中位边界误差、人工劳动，消除地层违规与不一致性。
AI中文摘要：
从测井数据中准确进行地下储层的地质特征描述对碳捕获与封存（CCS）、地热开发和自然资源开采等项目至关重要。现有自动技术主要使用滑动窗口分类，存在局限性。为此引入LithoFormer，它使用Seq2Seq变压器模型，一次摄取整个多变量测井数据。该框架利用带旋转位置嵌入（RoPE）的与通道无关的PatchTST主干来捕获长距离地质依赖关系。采用解耦多任务头联合预测地质分区和精确边界概率，通过地质信息损失函数强化物理约束。在三个真实数据集上验证和部署，与传统滑动窗口基线相比，LithoFormer将中位边界误差降低90%，消除地层顺序违规，减少80%人工专家劳动，消除地层不一致性，为大规模地下建模提供了可扩展且可靠的解决方案。
英文摘要：
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.
