Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …

Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to
reliable subsurface properties. However, it is challenging to acquire field data with an …

We propose an algorithm for seismic trace interpolation using generative adversarial
networks, a type of deep neural network. The method extracts feature vectors from the …

Seismic trace interpolation is an important technique because irregular or insufficient
sampling data along the spatial direction may lead to inevitable errors in multiple …

Seismic data processing algorithms greatly benefit from regularly sampled and reliable data.
Therefore, interpolation and denoising play a fundamental role as one of the starting steps of …

The reconstruction of seismic data with missing traces has been a long-standing issue in
seismic data processing. Deep learning (DL) has emerged as a popular tool for seismic …

Accurate forward modeling is essential for solving inverse problems in exploration
seismology. Unfortunately, it is often not possible to afford being physically or numerically …

Irregularity and coarse spatial sampling of seismic data strongly affect the performances of
processing and imaging algorithms. Therefore, interpolation is a usual preprocessing step in …

Exploration seismic data are heavily manipulated before human interpreters are able to
extract meaningful information regarding subsurface structures. This manipulation adds …