Seismic image denoising is essential to enhance the signal-to-noise ratio (SNR) of seismic
images and facilitate seismic processing and geological structure interpretation. With the …

Velocity analysis can be a time-consuming task when performed manually. Methods have
been proposed to automate the process of velocity analysis, which, however, typically …

Machine-learning (ML) applications in seismic exploration are growing faster than
applications in other industry fields, mainly due to the large amount of acquired data for the …

Seismic full waveform inversion is a common technique that is used in the investigation of
subsurface geology. Its classic implementation involves forward modeling of seismic …

Ground roll is a persistent problem in land seismic data. This type of coherent noise often
contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data …

Random noise attenuation is one of the most essential steps in seismic signal processing.
We propose a novel approach to attenuate seismic random noise based on deep …

We present a novel eikonal tomography approach using physics‐informed neural networks
(PINNs) for Rayleigh wave phase velocities based on the eikonal equation. The PINN …

For economic and efficiency reasons, blended acquisition of seismic data is becoming
increasingly commonplace. Seismic deblending methods are computationally demanding …

Seismic acquisition footprints appear as stably faint and dim structures and emerge fully
spatially coherent, causing inevitable damage to useful signals during the suppression …

We have trained a supervised deep 3D convolutional neural network (CNN) on marine
seismic images for poststack structural seismic image enhancement and noise attenuation …