Man-Recon: Manifold Learning For Reconstruction With Deep Autoencoder For Smart Seismic Interpretation

Deep learning can extract rich data representations if provided sufficient quantities of labeled training data. For many tasks however, annotating data has significant costs in terms of time and money, owing to the high standards of subject matter expertise required, for example in medical and geoph...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 2953 - 2957
Main Authors Mustafa, Ahmad, AlRegib, Ghassan
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.09.2021
Subjects
Active Learning
Autoencoders
Deep learning
Entropy
Interpretation
Learning systems
Manifold learning
Manifolds
Training
Training data
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Summary:Deep learning can extract rich data representations if provided sufficient quantities of labeled training data. For many tasks however, annotating data has significant costs in terms of time and money, owing to the high standards of subject matter expertise required, for example in medical and geophysical image interpretation tasks. Active learning can identify the most informative training examples for the interpreter to train, leading to higher efficiency. We propose an active learning method based on jointly learning representations for supervised and unsupervised tasks. The learned manifold structure is later utilized to identify informative training samples most dissimilar from the learned manifold from the error profiles on the unsupervised task. We verify the efficiency of the proposed method on a seismic facies segmentation dataset from the Netherlands F3 block survey, significantly outperforming contemporary methods to achieve the highest mean Intersection-Over-Union value of 0.773.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506657