Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

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 …

Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

Seismic coherence is of the essence for seismic interpretation as it highlights seismic
discontinuity features caused by the deposition process, reservoir boundaries, tectonic …

Low-frequency information is important in reducing the nonuniqueness of absolute
impedance inversion and for quantitative seismic interpretation. In traditional model-driven …

Deep learning has been successfully applied to image denoising. In this study, we take one
step forward by using deep learning to suppress random noise in poststack seismic data …

Lithology interpretation is important for understanding subsurface properties. Yet, the
common manual well log interpretation is usually with low efficiency and bad consistency …

Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and
complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious …

Time-frequency (TF) transforms are commonly used to analyze local features of
nonstationary seismic data and to help uncover structural or geologic information …

Noise attenuation has been a long-standing but still active topic in seismic data processing.
The deep convolutional neural networks (CNNs) have been recently adopted to remove the …