Epistemic Uncertainty and Model Transparency in Rock Facies Classification Using Monte Carlo Dropout Deep Learning
Author:
Affiliation:
1. Department of Geosciences, Centre for Subsurface Imaging (CSI), Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
2. Department of Computer and Information Sciences (CIS), Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
Funder
PETRONAS Research Sdn Bhd
Grant, Cost Center
Universiti Teknologi PETRONAS, Malaysia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10225733.pdf?arnumber=10225733
Reference33 articles.
1. Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks
2. Deep-learning seismic facies on state-of-the-art CNN architectures
3. Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach
4. Unsupervised seismic facies analysis via deep convolutional autoencoders
5. Lithological facies classification using deep convolutional neural network
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