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 …

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

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 …

Among the biggest challenges we face in utilizing neural networks trained on waveform (ie,
seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement …

The interpolation and reconstruction of missing traces are crucial steps in seismic data
processing; moreover, it is also a highly ill-posed problem, especially for complex cases …

How to represent a seismic wavefield? Traditionally, while seismic wavefields are
conceptualized continuously, acquisition geometries capture seismic data discretely using 2 …

Accurate interpolation of seismic data is crucial for improving the quality of imaging and
interpretation. In recent years, deep learning models such as U-Net and generative …

The knowledge of hidden resources present inside the Earth layers is vital for the
exploration of petroleum and hydrocarbons. However, the recorded seismic data are noisy …

Reservoir prediction is a significant issue in seismic interpretation, and it is difficult to reach a
tradeoff point for the reservoir prediction accuracy and spatial continuity. Nowadays, though …

Supervised convolutional neural networks (CNNs) are commonly used for seismic data
interpolation, in which a recovery network is trained over corrupted (input)/complete (label) …