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

Seismic inversion is one of the fundamental techniques for solving geophysics problems. To
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …

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

Deep learning (DL) has been widely used in seismic inversion. Since the label data
obtained in production are actually a small amount of 1-D well-log data, most DL-based …

Seismic inversion is aimed at building a mapping from low-resolution seismic data to high-
resolution impedance data. Most of the traditional methods have satisfactory interpretability …

Well log data plays a vital role in decision-making, resource assessment, production
optimization, and environmental management of oil and gas development. However, when …

Neural networks are commonly used for poststack and prestack seismic inversion. With
sufficient labeled data, the neural network-based seismic inversion results are more …

The application of artificial intelligence in seismic impedance inversion makes the prediction
of stratigraphic information more efficient. Semi-supervised framework for impedance …

Seismic inversion is a process to obtain the spatial structure and physical properties of
underground rock formations using surface acquired seismic data, constrained by known …

Deep learning methods, especially convolutional neural networks, achieve state-of-the-art
performance on seismic impedance inversion. Most of the methods are based on one …