Multi-resolution neural networks for tracking seismic horizons from few training images

Peters, Bas J M; Granek, Justin; Haber, Eldad

doi:10.48550/arxiv.1812.11092

Cited by 4 publications

(7 citation statements)

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“…Another work related to ours is [36]. Authors try to mitigate the high cost of manual annotations of seismic images by introducing an approach which can utilize sparse annotations instead of the commonly used dense segmentation masks.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks

Babakhin

Sanakoyeu

Kitamura

2019

Lecture Notes in Computer Science

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Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for the identification of hydrocarbon reservoirs and drill path planning. Unfortunately, the exact identification of large salt deposits is notoriously difficult and professional seismic imaging often requires expert human interpretation of salt bodies. Convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of seismic imaging. But the high cost of manual annotations by geophysics experts and scarce publicly available labeled datasets hinder the performance of the existing CNN-based methods. In this work, we propose a semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training. To reduce error amplification during self-training we propose a scheme which uses an ensemble of CNNs. We show that our approach outperforms state-of-theart on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods. The source code is available at GitHub.

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Section: Related Workmentioning

confidence: 99%

Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks

Babakhin

Sanakoyeu

Kitamura

2019

Lecture Notes in Computer Science

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“…We thus compute the full forward-propagation through the network using the full data image, f (θ, y), but the loss and gradient are based on a subset of pixels only. We train the network using Algorithm 1 from Peters et al [2018], which is stochastic gradient descent using one out of the 24 data and label images per iteration. We use the partial cross-entropy loss instead of the partial 1 -loss because we classify each pixel for the segmentation problem.…”

Section: Network and Partial Loss Functionmentioning

confidence: 99%

Automatic Classification of Geologic Units in Seismic Images Using Partially Interpreted Examples

Peters¹,

Granek²,

Haber³

2019

81st EAGE Conference and Exhibition 2019

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Geologic interpretation of large seismic stacked or migrated seismic images can be a time-consuming task for seismic interpreters. Neural network based semantic segmentation provides fast and automatic interpretations, provided a sufficient number of example interpretations are available. Networks that map from image-to-image emerged recently as powerful tools for automatic segmentation, but standard implementations require fully interpreted examples. Generating training labels for large images manually is time consuming. We introduce a partial loss-function and labeling strategies such that networks can learn from partially interpreted seismic images. This strategy requires only a small number of annotated pixels per seismic image. Tests on seismic images and interpretation information from the Sea of Ireland show that we obtain high-quality predicted interpretations from a small number of large seismic images. The combination of a partial-loss function, a multi-resolution network that explicitly takes small and large-scale geological features into account, and new labeling strategies make neural networks a more practical tool for automatic seismic interpretation.

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“…are the labeled features that need to be recovered. Using deep convolution networks is therefore a straight forward extension of existing neural network technology and have been studied recently by many authors (see for example [Peters et al, 2018, 2019, Wu and Zhang, 2018, Waldeland et al, 2018, Poulton, 2002, Leggett et al, 2003, Lowell and Paton, 2018, Zhao, 2018 and references within). However, while it seems straight forward to use such algorithms, there are some fundamental differences between vision-related applications to seismic processing.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for geophysicists and their application to seismic data interpretation

2019

Self Cite

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Neural-networks have seen a surge of interest for the interpretation of seismic images during the last few years. Network-based learning methods can provide fast and accurate automatic interpretation, provided there are sufficiently many training labels. We provide an introduction to the field aimed at geophysicists that are familiar with the framework of forward modeling and inversion. We explain the similarities and differences between deep networks to other geophysical inverse problems and show their utility in solving problems such as lithology interpolation between wells, horizon tracking and segmentation of seismic images. The benefits of our approach are demonstrated on field data from the Sea of Ireland and the North Sea.1

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Multi-resolution neural networks for tracking seismic horizons from few training images

Cited by 4 publications

References 0 publications

Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks

Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks

Automatic Classification of Geologic Units in Seismic Images Using Partially Interpreted Examples

Neural networks for geophysicists and their application to seismic data interpretation

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