This is the synthetic and field siesmic dataset used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". The dimensions of the large-scale and small-scale synthetic seismic datasets are 1600×256 pixels and 900×256 pixels. The field seismic data contains the subsets of the F3 Block, Australia Poseidon, Alaska North Slope seismic data
To train our deep convolutional neural network for Relative Geologic Time (RGT) estimation and fault...
Pattern recognition plays an important role in analyzing seismic reflection data, which contains val...
The optimization of inversion algorithms, coupled with increasing high-performance computing capabil...
This is the geological and geophysical forward modeling and modified CNN code for "ClinoformNet: str...
Implicit structural modeling using sparse and unevenly distributed data is essential for various sci...
The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning ...
This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural M...
Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelmi...
To train our deep convolutional neural network for paleokarst delineation in 3D seismic images, we a...
With the ever developing data acquisition techniques, seismic processing deals with massive amount o...
The advent of deep learning techniques had a huge impact in the geophysical community. Convolutional...
Open access to curated datasets positively impacts on scientific research of machine learning and de...
We examine a classification task in which signals of naturally occurring earthquakes are categorized...
With the dramatic growth and complexity of seismic data, manual seismic facies analysis has become a...
Identifying the geological structures in seismic volumes is of great importance for oil and gas expl...
To train our deep convolutional neural network for Relative Geologic Time (RGT) estimation and fault...
Pattern recognition plays an important role in analyzing seismic reflection data, which contains val...
The optimization of inversion algorithms, coupled with increasing high-performance computing capabil...
This is the geological and geophysical forward modeling and modified CNN code for "ClinoformNet: str...
Implicit structural modeling using sparse and unevenly distributed data is essential for various sci...
The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning ...
This is training and validation datasets used in manuscript "Three-Dimensional Implicit Structural M...
Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelmi...
To train our deep convolutional neural network for paleokarst delineation in 3D seismic images, we a...
With the ever developing data acquisition techniques, seismic processing deals with massive amount o...
The advent of deep learning techniques had a huge impact in the geophysical community. Convolutional...
Open access to curated datasets positively impacts on scientific research of machine learning and de...
We examine a classification task in which signals of naturally occurring earthquakes are categorized...
With the dramatic growth and complexity of seismic data, manual seismic facies analysis has become a...
Identifying the geological structures in seismic volumes is of great importance for oil and gas expl...
To train our deep convolutional neural network for Relative Geologic Time (RGT) estimation and fault...
Pattern recognition plays an important role in analyzing seismic reflection data, which contains val...
The optimization of inversion algorithms, coupled with increasing high-performance computing capabil...