构建首个物理驱动的地震气烟囱数据集，助力油气勘探与钻井安全
SIGMA: A Physics-Based Benchmark for Gas Chimney Understanding in Seismic Images
- 基于物理模拟生成真实地质场景下的地震图像与气烟囱标注
- 包含成对退化与真实图像，支持检测与增强任务
- 适用于地震图像理解、油气勘探及深度学习模型评估
地震图像通过野外记录重构地下反射率，用于油气勘探与储层监测。气烟囱是由地下流体运移引起的垂直异常，其理解对评估油气潜力和规避钻井风险至关重要。然而，强地震衰减与散射使得准确检测困难。传统物理方法计算成本高且对模型误差敏感，深度学习虽高效但缺乏标注数据。本文提出 extbf{SIGMA}，一个面向地震图像中气烟囱理解的新物理基准数据集，包含：(i) 像素级气烟囱掩码，用于检测；(ii) 成对退化与真实图像，用于增强。采用覆盖多种地质条件与采集环境的物理方法生成数据。大量实验表明，SIGMA 是气烟囱解释的挑战性基准，有助于提升地震图像的通用理解能力。
原文摘要 · Abstract (English)
Seismic images reconstruct subsurface reflectivity from field recordings, guiding exploration and reservoir monitoring. Gas chimneys are vertical anomalies caused by subsurface fluid migration. Understanding these phenomena is crucial for assessing hydrocarbon potential and avoiding drilling hazards. However, accurate detection is challenging due to strong seismic attenuation and scattering. Traditional physics-based methods are computationally expensive and sensitive to model errors, while deep learning offers efficient alternatives, yet lacks labeled datasets. In this work, we introduce \textbf{SIGMA}, a new physics-based dataset for gas chimney understanding in seismic images, featuring (i) pixel-level gas-chimney mask for detection and (ii) paired degraded and ground-truth image for enhancement. We employed physics-based methods that cover a wide range of geological settings and data acquisition conditions. Comprehensive experiments demonstrate that SIGMA serves as a challenging benchmark for gas chimney interpretation and benefits general seismic understanding.
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