Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation

TL;DR

This paper introduces Man-recon, an active learning approach using manifold learning with deep autoencoders to efficiently select training samples for seismic interpretation, significantly reducing annotation costs.

Contribution

It presents a novel active learning method that combines supervised and unsupervised representation learning to identify the most informative samples based on manifold dissimilarity.

Findings

Achieved a mean Intersection-Over-Union of 0.773 on seismic data

Outperformed existing active learning methods in seismic interpretation

Demonstrated efficiency in reducing annotation effort

Abstract

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.