Literature review on deep learning for the segmentation of seismic images

Literature review on deep learning for the segmentation of seismic images

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 particular focus on facies segmentation. We begin by comparing the contributions of DL to traditional techniques used...

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Bibliographic Details
Published in:Earth-science reviews Vol. 258; p. 104955
Main Authors: Monteiro, Bruno A.A., Canguçu, Gabriel L., Jorge, Leonardo M.S., Vareto, Rafael H., Oliveira, Bryan S., Silva, Thales H., Lima, Luiz Alberto, Machado, Alexei M.C., Schwartz, William Robson, Vaz-de-Melo, Pedro O.S.
Format: Journal Article
Language:English
Published: Elsevier B.V 01-11-2024
Subjects:
Deep learning
Literature review
Segmentation
Seismic facies
Seismic image
Semantic segmentation
CNN
BNN
Segmentation
Literature review
ABL
SVM
TVL
GPU
Deep learning
ADV
SLIC
Seismic facies
FWIoU
MSE
Seismic image
ML
CON
LSTM
Semantic segmentation
MCA
CEL
ASPP
SSL
CS
mIoU
PA
SGD
GAN
RMV
ABUnet
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Summary: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 particular focus on facies segmentation. We begin by comparing the contributions of DL to traditional techniques used in seismic image interpretation. The review then explores the learning paradigms, architectures, loss functions, public datasets, and evaluation metrics employed in seismic data segmentation. While supervised learning remains the dominant approach, recent years have seen a growing interest in semi-supervised and unsupervised methods to address the challenge of limited labeled data. Additionally, we found that the U-Net architecture is the most prevalent backbone for semantic segmentation, appearing in one-third of the articles reviewed. We also present a comprehensive compilation of the results obtained by 24 methods and discuss the challenges and research opportunities in this field. Notably, the lack of standardized protocols for performance comparison, combined with variability in datasets and evaluation metrics across studies, raises questions about what truly constitutes the current state of the art in semantic segmentation of seismic data.
ISSN:0012-8252
DOI:10.1016/j.earscirev.2024.104955