Seismic data reconstruction is one of the essential steps in the seismic data processing.
Recently, the deep learning (DL) models have attracted huge attention in seismic …

We develop a deep learning framework based on deep image prior (DIP) and attention
networks for 3-D seismic data enhancement. First, the 3-D noisy data are divided into …

Machine learned tasks on seismic data are often trained sequentially and separately, even
though they utilize the same features (ie, geometrical) of the data. We present StorSeismic …

We propose to denoise and reconstruct the 3D seismic data simultaneously using an
unsupervised deep learning (DL) framework, which does not require any prior information …

Deep learning (DL) has achieved promising results for impedance inversion via seismic
data. Generally, these networks, composed of convolution layers and residual blocks, tend …

Seismic data reconstruction is always an essential step in the field of seismic data
processing. Effective reconstruction methods can obtain high-density information at low-cost …

Seismic fault interpretation is of extraordinary significant for hydrocarbon reservoir
characterization and drilling hazard mitigation. In recent years, deep learning-based seismic …

We propose to use unsupervised deep learning (DL) and attention networks to mute the
unwanted components of the single-channel earthquake data. The proposed algorithm is an …

Missing traces interpolation is a basic step in the seismic data processing workflow.
Recently, many seismic data interpolation methods based on different neural networks have …

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