A Deep-Learning-Based Targeted Interpolation Method for Seismic Data: A Consecutively Missing Trace VSP Case

Abstract

Seismic data interpolation is an important processing method for improving the quality of seismic data. Traditional interpolation methods often face limitations due to their dependence on prior information and their challenges in processing continuous missing data. Vertical seismic profiling (VSP) data, owing to its unique acquisition approach, generally do not suffer from missing receivers but can have missing shots, with the locations of these missing shots being known. To address this specific issue of missing shots, a specialized interpolation technique has been proposed for targeted missing data. This technique involves creating datasets from the original complete data that are tailored to fixed missing shot scenarios, allowing for a more effective application of the trained network to field data. In addition, we have optimized the network structure based on UNet to meet the specific requirements for handling consecutive gaps. Both synthetic and field data demonstrate the effectiveness of this targeted interpolation method.