Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Abstract Domain adaptation (DA) is the topical problem of adapting models from labelled
source datasets so that they perform well on target datasets where only unlabelled or …

Seismic facies analysis is a crucial foundation for basin-fill studies and oil and gas
exploration. With its rapid development, convolutional neural network (CNN)-assisted …

Several published solutions exist for the automatization of seismic facies labeling. We
suggest an approach that applies tools from deep learning and semantic image …

The use of convolutional neural networks (CNNs) in seismic interpretation tasks, like facies
classification, has garnered a lot of attention for its high accuracy. However, its drawback is …

With the successful application and breakthrough of deep learning technology in image
segmentation, there has been continuous development in the field of seismic facies …

With the great success of deep learning in computer vision, the application of convolution
neural network (CNN) in seismic facies classification is growing rapidly. However, most of …

The use of machine learning for seismic interpretation is a growing area of interest for
researchers. Manual interpretation demands time and specialized effort. The use of machine …

This systematic literature review provides a comprehensive overview of the current state of
deep learning (DL) specifically targeted at semantic segmentation in seismic data, with a …