Waveform embedding: Automatic horizon picking with unsupervised deep learning

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Abstract

Picking horizons from seismic images is a fundamental step that could critically impact seismic interpretation quality. We have developed an unsupervised approach, waveform embedding, based on a deep convolutional autoencoder network to learn to transform seismic waveform samples to a latent space in which any waveform can be represented as an embedded vector. The regularizing mechanism of the autoencoder ensures that similar waveform patterns are mapped to embedded vectors with a shorter distance in the latent space. Within a search region, we transform all of the waveform samples to the latent space and compute their corresponding distance to the embedded vector of a control point that is set to the target horizon. We then convert the distance to a horizon probability map that highlights where the horizon is likely to be located. This method can guide horizon picking across lateral discontinuities such as faults, and it is insensitive to noise and lateral distortions. In addition, our unsupervised learning algorithm requires no training labels. We apply our horizon-picking method to multiple 2D/3D examples and obtain results more accurate than the baseline method.

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Leveraging uncertainty information from deep neural networks for disease detection

Christian Leibig, Vaneeda Allken, Murat Ayhan … (2017)

Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task/dataset difficulty, we surpass 85% sensitivity and 80% specificity as recommended by the NHS when referring 0−20% of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust.