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Automatic channel detection using deep learning

Nam Pham, Sergey Fomel and Dallas Dunlap
Automatic channel detection using deep learning
Interpretation (Tulsa) (August 2019) 7 (3): SE43-SE50

Abstract

We have developed a method based on an encoder-decoder convolutional neural network for automatic channel detection in 3D seismic volumes. We use two architectures borrowed from computer vision: SegNet for image segmentation together with Bayesian SegNet for uncertainty measurement. We train the network on 3D synthetic volumes and then apply it to field data. We test the proposed approach on a 3D field data set from the Browse Basin, offshore Australia, and a 3D Parihaka seismic data in New Zealand. Applying the weights estimated from training on 3D synthetic volumes to a 3D field data set accurately identifies channel geobodies without the need for any human interpretation on seismic attributes. Our proposed method also produces uncertainty volumes to quantify the trustworthiness of the detection model.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 7
Serial Issue: 3
Title: Automatic channel detection using deep learning
Affiliation: University of Texas at Austin, John A. and Katherine G. Jackson School of Geosciences, Austin, TX, United States
Pages: SE43-SE50
Published: 201908
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 18
Accession Number: 2020-010976
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Annotation: Special section: Machine learning in seismic data analysis
Illustration Description: illus. incl. sects., sketch maps
S16°00'00" - S12°00'00", E119°00'00" - E126°00'00"
Secondary Affiliation: Bureau of Economic Geology, USA, United States
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 202008
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