Abstract Machine learning-based seismic processing models are typically trained separately
to perform seismic processing tasks (SPTs) and, as a result, require plenty of high-quality …

As the smart oilfield has grown, various deep learning technologies are being utilized to
recognize the graphic feature of the indicator diagram in order to detect the fault type of rod …

Diffraction images can be used for modeling reservoir heterogeneities at or below the
seismic wavelength scale. However, the extraction of diffractions is challenging because …

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for
estimating seismic wavefield solutions due to their typical mesh-free and unsupervised …

Conventional interpretation of airborne electromagnetic data has been conducted by solving
the inverse problem. However, with recent advances in machine learning (ML) techniques, a …

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for
estimating seismic wavefield solutions due to their typical mesh-free and unsupervised …

Precise first-arrival picking holds pivotal importance in the realms of seismic data processing
and microseismic monitoring. Recently, data-driven approaches have shown remarkable …

In this study, we investigate the use of an ensemble of deep learning models to improve the
quality and efficiency of seismic first break event picking. In traditional workflows, we often …