Seismic coherence for discontinuity interpretation
Seismic coherence is of the essence for seismic interpretation as it highlights seismic
discontinuity features caused by the deposition process, reservoir boundaries, tectonic …
discontinuity features caused by the deposition process, reservoir boundaries, tectonic …
ADDCNN: An attention-based deep dilated convolutional neural network for seismic facies analysis with interpretable spatial–spectral maps
With the dramatic growth and complexity of seismic data, manual seismic facies analysis has
become a significant challenge. Machine learning and deep learning (DL) models have …
become a significant challenge. Machine learning and deep learning (DL) models have …
K-means clustering using principal component analysis to automate label organization in multi-attribute seismic facies analysis
The use of unsupervised machine learning methods such as K-means, Hierarchical
Agglomerative Clustering, and Self-organizing maps is constantly increasing in seismic …
Agglomerative Clustering, and Self-organizing maps is constantly increasing in seismic …
Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir
Conventional geological modelling methods are not capable to provide precise and
comprehensive model of the subsurface structures, when dealing with insufficient data …
comprehensive model of the subsurface structures, when dealing with insufficient data …
Detection and identification of cyber and physical attacks on distribution power grids with pvs: An online high-dimensional data-driven approach
Cyber and physical attacks threaten the security of distribution power grids. The emerging
renewable energy sources such as photovoltaics (PVs) introduce new potential …
renewable energy sources such as photovoltaics (PVs) introduce new potential …
[HTML][HTML] Seismic Image Identification and Detection Based on Tchebichef Moment Invariant
A Lu, B Honarvar Shakibaei Asli - Electronics, 2023 - mdpi.com
The research focuses on the analysis of seismic data, specifically targeting the detection,
edge segmentation, and classification of seismic images. These processes are fundamental …
edge segmentation, and classification of seismic images. These processes are fundamental …
Convolutional neural networks as aid in core lithofacies classification
Artificial intelligence methods have a very wide range of applications. From speech
recognition to self-driving cars, the development of modern deep-learning architectures is …
recognition to self-driving cars, the development of modern deep-learning architectures is …
Seismic expression and geomorphology of igneous bodies: A Taranaki Basin, New Zealand, case study
L Infante-Paez, KJ Marfurt - Interpretation, 2017 - library.seg.org
Very little research has been done on volcanic rocks by the oil industry due to the
misconception that these rocks cannot be “good reservoirs.” However, in the past two …
misconception that these rocks cannot be “good reservoirs.” However, in the past two …
Unsupervised contrastive learning for seismic facies characterization
Seismic facies characterization plays a key role in hydrocarbon exploration and
development. The existing unsupervised methods are mostly waveform-based and involve …
development. The existing unsupervised methods are mostly waveform-based and involve …
Evaluation of principal component analysis for reducing seismic attributes dimensions: Implication for supervised seismic facies classification of a fluvial reservoir from …
Because of their effectiveness in identifying geologic features, seismic attributes are usually
used as input to machine learning (ML) models for facies classification. Typically, too many …
used as input to machine learning (ML) models for facies classification. Typically, too many …