In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Denoising is an indispensable step in seismic data processing. Deep-learning-based
seismic data denoising has been recently attracting attentions due to its outstanding …

Irregularity and coarse spatial sampling of seismic data strongly affect the performances of
processing and imaging algorithms. Therefore, interpolation is a usual preprocessing step in …

Seismic data processing requires careful interpolation or reconstruction to restore the
regularly or irregularly missing traces. In practice, seismic data with consecutively missing …

Distributed acoustic sensing (DAS) has been considered a breakthrough technique in
seismic data collection owing to its advantages in acquisition cost and accuracy. However …

Blended acquisition is still open to improve the efficiency of seismic data acquisition.
Deblending is an essential procedure to provide separated gathers for subsequent …

Existing deep learning-based seismic data denoising methods mainly involve supervised
learning, in which a denoising network is trained using a large amount of noisy input/clean …

We use techniques from Bayesian inference and deep neural networks to translate
uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as …

Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow
to obtain high-quality velocity models and seismic images. Building on the idea of describing …

The simultaneous source acquisition method, which excites multiple sources in a narrow
time interval, can greatly improve the efficiency of seismic data acquisition and provide good …