发表机构
University of Oklahoma; Information Technology University; King Fahd University of Petroleum and Minerals（俄克拉荷马大学; 信息技术大学; 法赫德国王石油矿产大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DeepStratNet框架，将地震层位追踪转化为有界坐标回归问题，利用轻量回归头与LSTM建模切片上下文，在稀疏标签下优于分割方法，并具备鲁棒性与自动质量控制能力。
AI 中文摘要
自动层位追踪是三维地震解释中的一项基础任务。现有的大多数深度学习方法将其表述为密集语义分割，通常采用基于U-Net的架构。模型生成覆盖所有像素的概率图，必须经过后处理才能提取精确的层位坐标，而时间/深度域中的层位拾取必须转换为密集掩码用于训练。因此，未拾取的地震道被当作背景处理，这可能阻碍收敛，并且前后处理都可能将误差引入最终解释结果。此外，二维分割模型本质上无法捕获切片间的上下文，而三维模型通常计算代价过高。我们转而将层位追踪表述为一个有界坐标回归问题，其中模型直接预测目标层位在每个横向位置的时间/深度坐标。我们提出了一种轻量级回归头，可与任何预训练视觉骨干兼容，并结合LSTM模块来建模切片间上下文，从而在整个体上生成连续的层位面。L1和L2损失的组合在有效层位拾取处监督预测，而地质信息正则化强制相邻道之间的横向连续性。在受控实验条件下，我们在来自新西兰的地震体上评估了四种预训练视觉骨干在分割和回归两种配置下的表现。所提出的方法在定量上（使用RMSE和PCC等指标）和定性上均持续优于其分割对应方法，同时在对训练拾取稀疏性增加时表现出更强的鲁棒性。最后，我们表明相邻道之间的预测变化能够捕获地质复杂性的局部变化，为下游地震解释提供自动质量控制措施。
英文摘要
Automatic horizon tracking is a foundational task in 3D seismic interpretation. Most existing deep learning approaches formulate it as dense semantic segmentation, typically using U-Net-based architectures. The model produces a probability map over all pixels that must be post-processed to extract precise horizon coordinates, while horizon picks in time/depth must be converted into dense masks for training. Unpicked seismic traces are consequently treated as background, which can hinder convergence, and both pre- and post-processing can introduce errors into the final interpretation. Moreover, 2D segmentation models do not inherently capture inter-slice context, while 3D models are often computationally prohibitive. We instead formulate horizon tracking as a bounded coordinate regression problem, where the model directly predicts the time/depth coordinate of the target horizon at each lateral position. We propose a lightweight regression head compatible with any pretrained vision backbone, coupled with an LSTM module to model inter-slice context and produce a continuous horizon surface across the volume. A combination of L1 and L2 losses supervises predictions at valid horizon picks, while a geology-informed regularization enforces lateral continuity between successive traces. Under controlled experimental conditions, we evaluate four pretrained vision backbones under both segmentation and regression configurations on a seismic volume from New Zealand. The proposed approach consistently outperforms its segmentation counterparts quantitatively, using metrics including RMSE and PCC, and qualitatively, while also demonstrating greater robustness to increasing sparsity of training picks. Finally, we show that prediction variation across successive traces captures local variations in geological complexity, providing an automated quality control measure for downstream seismic interpretation.
