Seismic Data Reconstruction with Multiscale Attention Deep Learning

This paper presents a novel deep learning-based approach to reconstruct seismic data from incomplete observations. The proposed method utilizes a multiscale attention mechanism to learn the spatial correlation between the incomplete observations and the full seismic data. The method is tested on a 3D seismic data set from an oil reservoir and is demonstrated to produce improved results compared to existing deep learning methods. Additionally, the paper provides an in-depth analysis of the performance of the proposed approach, showing that the use of multiscale attention significantly improves the accuracy of the reconstruction. The results of the paper demonstrate the potential of the proposed method for seismic data reconstruction.