At present, machine sense of smell has shown its important role and advantages in many
scenarios. The development of machine sense of smell is inseparable from the support of …

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

This study analyses the main challenges, trends, technological approaches, and artificial
intelligence methods developed by new researchers and professionals in the field of …

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 …

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 …

Missing trace reconstruction is an essential step in the seismic data processing. Various
interpolation methods have been proposed for handling this issue. In recent years, deep …

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

Three-dimensional (3-D) gravity inversion obtains the density distribution of subsurface
geological bodies through observed gravity anomalies. Recently, data-driven methods …

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 …

Recently, approaches based on deep learning are quite prevalent in the area of remote
sensing scene classification. Though significant success has been achieved, these …