Automatic Estimation of Fresnel Zones in Migrated Dip-Angle Gathers Using Semantic Segmentation Model.

Implementing Fresnel zones-based stacking is a pursuit in various seismic imaging methods. However, the estimation of Fresnel zones remains a challenge in three-dimensional migration. Migrated dip-angle gathers provide a visible domain for estimating Fresnel zones. An analytical estimation (i.e., a model-driven method) faces limitations in automatically estimating Fresnel zones in migrated dip-angle gathers in real-world situations due to complex reflections, non-uniform coverage, and noises. Human interaction remains necessary for precisely estimating Fresnel zones in migrated dip-angle gathers. Due to the high number of Fresnel zones in 3D cases, interpolation is necessary to fill in the gaps between manually estimated zones. Aiming to reduce the workload of human interaction and mitigate interpolation errors, we propose a semantic segmentation model (i.e., a deep learning-based data-driven method) to estimate Fresnel zones in migrated dip-angle gathers automatically. We transform the estimation of Fresnel zones into a binary classification task of each pixel in dip-angle gathers. Instead of training the network using the entire dip-angle gather images, we train the network using patches to make the network focus on learning the detailed and general features within the patches. Our proposed network, named deep-supervised attention-UNet, is trained using a deep-supervised method along with a hierarchical hybrid loss function to segment the dip-angle gather on different scales. This approach yields superior segmentation results compared to the UNet model, in qualitative and quantitative aspects. We test the efficiency and practicability of our method using a marine field data set. The signal-to-noise ratio (SNR) of migration results obtained using the Fresnel zones estimated by our method is improved significantly.