Seismic Data Reconstruction with Multiscale Attention Deep Learning: A Novel Approach

This paper presents a novel seismic data reconstruction method based on multiscale attention deep learning. Traditional seismic data reconstruction methods often employ interpolation or model prediction techniques, but these methods often struggle to handle noise and uncertainty present in the data. To address this challenge, we propose a new approach that utilizes multiscale attention mechanisms within deep learning, enabling the effective extraction of useful information from noisy and uncertain data, resulting in more accurate and reliable seismic data reconstruction. Experimental results demonstrate the significant advantages of our proposed method over traditional approaches in enhancing the accuracy and stability of data reconstruction, providing more reliable data support for seismic exploration and geological research.