Meta‐Interpolation: An Efficient Seismic Data Interpolation Framework for Adaptive Spatial Continuity Modelling

Abstract

Seismic data interpolation is crucial for improving data quality and ensuring reliable subsurface interpretation. While deep learning methods have shown strong potential for this task, they often struggle to preserve the spatial continuity and global consistency of seismic data under complex missing conditions, leading to amplitude distortion and reduced accuracy in subsequent geological interpretation and reservoir evaluation. To address this issue, we propose Meta‐Interpolation, a novel adaptive framework that explicitly models spatial continuity by leveraging prior knowledge from complete seismic data. Specifically, we design a dual‐branch architecture comprising an auxiliary network (AN) and an interpolation network (IN). The AN is pre‐trained on complete seismic data to capture spatially continuous feature representations. Rather than relying solely on a direct mapping from incomplete data during training, our framework employs a knowledge distillation strategy where the AN acts as a teacher to guide the IN. To ensure this guidance is optimal and adaptive to varying missing patterns, we introduce a meta‐network (MN). The MN dynamically generates channel‐wise weights and regulates the distillation process, effectively guiding the IN to capture both global consistency and local spatial continuity. Extensive experiments conducted under random missing, consecutive missing and noisy data missing scenarios demonstrate that the proposed framework significantly improves the quality, efficiency and generalization of seismic interpolation.