发表机构
Bureau of Economic Geology, The University of Texas at Austin; SLB（德克萨斯大学奥斯汀分校经济地质局; 斯伦贝谢）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于DDPM的断层条件地震图像生成框架，开发U-DDPM与FC-DDPM模型，联合标准化多数据集训练，实现可控的地震数据增强，解决地震解释中训练集不足的瓶颈。
AI 中文摘要
地震数据增强对于训练用于断层检测与表征的鲁棒深度学习（DL）模型至关重要，但传统方法仍存在不足。几何变换（如翻转或旋转）与噪声注入无法捕捉真实地震纹理，而基于物理的正演模拟计算成本高昂，且常与野外数据的统计特征匹配度较差。我们提出一种去噪扩散概率模型（DDPM）框架，通过迭代噪声注入与学习去噪过程直接从野外数据中学习地震图像的高维分布。我们开发了两种变体：生成真实叠后地震 patch 的无条件模型（U-DDPM），以及生成符合用户指定的、以二值掩码编码的断层几何形态的多样地震实现的断层条件模型（FC-DDPM）。我们联合标准化了三个具有不同振幅尺度与处理历史的开源野外数据集用于训练，展示了跨测线泛化能力。U-DDPM 生成的图像复现了真实地震数据中观测到的主导纹理模式、振幅分布与反射层连续性；FC-DDPM 在保持与施加的断层掩码结构保真度的同时，在周围地震纹理中引入了真实的变异性，实现了从单一结构模板到多样实现的一对多映射。频率-波数谱分析证实，合成样本保留了训练分布的时空带宽与倾角内容。该框架提供了一种实用、可控的增强机制，可在无需计算成本高昂的正演模拟或测线特定参数调优的情况下，扩充有限的带断层标注的训练集，直接解决基于 DL 的地震解释中的关键瓶颈问题。
英文摘要
Seismic data augmentation is critical for training robust deep learning (DL) models for fault detection and characterization, yet conventional approaches remain inadequate. Geometric transforms, e.g., flips or rotations, and noise injection fail to capture realistic seismic textures, while physics-based forward modeling is computationally expensive and often poorly aligned with the statistical characteristics of field data. We present a denoising diffusion probabilistic model (DDPM) framework that learns the high-dimensional distribution of seismic images directly from field data via iterative noise injection and learned denoising. We develop two variants: an unconditional model (U-DDPM) that generates realistic post-stack seismic patches and a fault-conditioned model (FC-DDPM) that produces diverse seismic realizations that honor user-specified fault geometries encoded as binary masks. Three open-source field datasets with differing amplitude scales and processing histories are jointly standardized for training, demonstrating cross-survey generalization. Generated images from U-DDPM reproduce the dominant textural patterns, amplitude distributions, and reflector continuity observed in real seismic data. FC-DDPM maintains structural fidelity to imposed fault masks while introducing realistic variability in surrounding seismic textures, enabling one-to-many realizations from a single structural template. Frequency-wavenumber spectral analysis confirms that synthetic samples preserve the spatiotemporal bandwidth and dip content of the training distribution. The proposed framework provides a practical, controllable augmentation mechanism for expanding limited fault-labeled training sets without computationally expensive forward modeling or survey-specific parameter tuning, directly addressing a key bottleneck in DL-based seismic interpretation.
Comments28 pages, 16 figures. Submitted to Geophysics
