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Multiresolution neural networks for tracking seismic horizons from few training images

Bas Peters, Justin Granek and Eldad Haber
Multiresolution neural networks for tracking seismic horizons from few training images
Interpretation (Tulsa) (August 2019) 7 (3): SE201-SE213


Detecting a specific horizon in seismic images is a valuable tool for geologic interpretation. Because hand picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Until now, most networks have been trained on data that were created by cutting larger seismic images into many small patches. This limits the networks ability to learn from large-scale geologic structures. Moreover, currently available networks and training strategies require label patches that have full and continuous horizon picks (annotations), which are also time-consuming to generate. We have developed a projected loss function that enables training on labels with just a few annotated pixels and has no issue with the other unknown label pixels. We use this loss function for training convolutional networks with a multiresolution structure, including variants of the U-net. Our networks learn from a small number of large seismic images without creating patches. Training uses all seismic data without reserving some for validation. Only the labels are split into training/testing. We validate the accuracy of the trained network using the horizon picks that were never shown to the network. Contrary to other work on horizon tracking, we train the network to perform nonlinear regression, not classification. As such, we generate labels as the convolution of a Gaussian kernel and the known horizon locations that communicate uncertainty in the labels. The network output is the probability of the horizon location. We examine the new method on two different data sets, one for horizon extrapolation and another data set for interpolation. We found that the predictions of our methodology are accurate even in areas far from known horizon locations because our learning strategy exploits all data in large seismic images.

ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 7
Serial Issue: 3
Title: Multiresolution neural networks for tracking seismic horizons from few training images
Affiliation: University of British Columbia, Vancouver, BC, Canada
Pages: SE201-SE213
Published: 201908
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 50
Accession Number: 2020-011115
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Annotation: Part of a special section on Machine learning in seismic data analysis, edited by Zeng, H.
Illustration Description: illus. incl. 1 table
Secondary Affiliation: Computational Geosciences, CAN, Canada
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute.
Update Code: 202008
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