Component reliability plays a pivotal role in industrial systems, which are evolving with
larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and …

One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

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 …

Deep clustering seeks to group data into distinct clusters using deep learning techniques.
Existing approaches of deep clustering can be broadly categorized into two groups: offline …

Geological and geophysical interpretation is characterised by large and localised datasets
that are extremely expensive to acquire. There are clear advantages in applying deep …

Traditional seismic facies analysis, which depends on manual interpretation of seimic
amplitude, encounters difficulties because of the complexity, volume, and limited resolution …

Seismic data quality proves pivotal to its interpretation, necessitating the reduction of noise
and enhancement of resolution. Traditional and deep-learning-based solutions have …

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 …

Seismic facies classification is crucial in reservoir evaluation and guiding oil and gas
exploration and development. While machine learning and deep learning models have …

Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for
various applications, including hydrocarbon, geothermal, and CO 2 sequestration projects …