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Waveform embedding; automatic horizon picking with unsupervised deep learning

Yunzhi Shi, Wu Xinming and Sergey Fomel
Waveform embedding; automatic horizon picking with unsupervised deep learning
Geophysics (May 2020) 85 (4): WA67-WA76

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.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 85
Serial Issue: 4
Title: Waveform embedding; automatic horizon picking with unsupervised deep learning
Affiliation: University of Texas at Austin, Austin, TX, United States
Pages: WA67-WA76
Published: 20200508
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 44
Accession Number: 2020-049511
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Secondary Affiliation: University of Science and Technology of China, CHN, China
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 202014
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