SIGMA: A Physics-Based Benchmark for Gas Chimney Understanding in Seismic Images

Abstract

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.

Figures (8)

• Figure 1: Visualization of a 3D seismic volume. The 2D seismic slices (expressed in kilometers) are extracted from this volume, with the gas chimney regions highlighted by the yellow bounding boxes along with their corresponding velocity models.
• Figure 2: An example seismic data with gas chimney areas.
• Figure 3: Seismic image construction. (a) An illustration of seismic image acquisition with gas chimney effect. The sound waves become distorted when they pass through the gas chimney area. (b) The final seismic image after migration, showing that the gas chimney area leads to poor seismic attribute quality.
• Figure 4: Dataset creation pipeline. (a) Overview of seismic image construction framework with gas chimney. (b) Step-by-step dataset generation process: starting from an original velocity model, a random fracture network is generated, followed by gas saturation simulation through a physically grounded modeling stage. The gas-affected velocity model is then constructed, and final seismic images are synthesized using reverse time migration.
• Figure 5: Data samples. We present samples from our SIGMA, each sample consists of original velocity model, gas saturation map, gas-affected velocity model, gas-affected seismic image and clean seismic image. The yellow bounding boxes highlight the differences between normal and gas-affected regions.