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.

翻译：地震图像通过野外记录重建地下反射率，指导勘探与储层监测。气烟囱是由地下流体运移引起的垂向异常现象。理解这些现象对于评估油气潜力及规避钻井风险至关重要。然而，由于强地震衰减和散射效应，精确检测极具挑战性。传统基于物理的方法计算成本高昂且对模型误差敏感，而深度学习虽然提供了高效替代方案，却缺乏标注数据集。本研究提出\textbf{SIGMA}——面向地震图像中气烟囱理解的物理基准数据集，具有以下特点：（i）用于检测的像素级气烟囱掩膜；（ii）用于图像增强的配对退化图像与真实图像。我们采用覆盖广泛地质背景与数据采集条件的物理方法。综合实验表明，SIGMA可作为气烟囱解译的挑战性基准，并有益于通用地震理解任务。