The interpolation and reconstruction of missing traces are crucial steps in seismic data
processing; moreover, it is also a highly ill-posed problem, especially for complex cases …

Supervised convolutional neural networks (CNNs) are commonly used for seismic data
interpolation, in which a recovery network is trained over corrupted (input)/complete (label) …

Seismic data interpolation is an essential procedure in seismic data processing. However,
conventional interpolation methods may generate inaccurate results due to the simplicity of …

Seismic data often suffer from missing traces, and traditional reconstruction methods are
cumbersome in parameterization and struggle to handle large-scale missing data. While …

Reconstructing missing seismic data is crucial for seismic processing and interpretation.
Recent methods struggle when seismic traces are regularly missing, such as near-offset …

One of the major hot topics in seismic data processing is the reconstruction of successively
sampled seismic data. There are numerous traditional methods proposed for addressing this …

Deep learning has been widely applied to seismic data interpolation. However, most
existing methods are based on supervised learning, suffering from limitations such as low …

Convolutional Neural Network (CNN) is widely used for seismic data recovery, and has
demonstrated remarkable performance in reconstructing irregularly and regularly sampled …

The acquisition and reconstruction of seismic data are fundamental to seismic exploration.
The balancing data quality and acquisition cost is essential. To address this challenge, we …

The truncated singular value decomposition (SVD), also known as the best low-rank matrix
approximation with minimum error measured by a unitarily invariant norm, has been applied …