Seismic Stratum Segmentation Using an Encoder–Decoder Convolutional Neural Network

As an essential step in reservoir characterization, seismic stratigraphic interpretation is often dependent on the development of powerful computer-based interpretation tools that can simulate the intelligence of experienced interpreters. With the success of machine/deep learning applications in man...

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Vydané v:Mathematical geosciences Ročník 53; číslo 6; s. 1355 - 1374
Hlavní autori: Wang, Detao, Chen, Guoxiong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021
Springer Nature B.V
Predmet:
Artificial neural networks
Bayesian analysis
Chemistry and Earth Sciences
Coders
Computer Science
Deep learning
Earth and Environmental Science
Earth Sciences
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Image classification
Image segmentation
Intelligence
Interpreters
Learning algorithms
Machine learning
Mathematical models
Neural networks
Physics
Pixels
Probability theory
Reservoirs
Seismic activity
Statistics for Engineering
Stratigraphy
Training
Deep learning
Seismic interpretation
Stratum segmentation
U-Net
ISSN:1874-8961, 1874-8953
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Shrnutí:As an essential step in reservoir characterization, seismic stratigraphic interpretation is often dependent on the development of powerful computer-based interpretation tools that can simulate the intelligence of experienced interpreters. With the success of machine/deep learning applications in many aspects of geoscience in recent decades, geophysicists have become more dedicated to exploring seismic big data in a smarter and more sophisticated way to better image subsurface reservoirs/structures. In this paper, a specific U-shaped fully convolutional network (U-Net) is established for automatic seismic stratigraphic interpretation. Specifically, this task is formulated as a semantic segmentation problem by identifying strata at the pixel level and classifying each pixel in the image into a specific stratum category. An experiment using the Netherlands F3 seismic dataset suggests that, compared with previously established deep learning models requiring a large number of training sets, the proposed U-Net method can achieve high evaluation indicators and better stratum segmentations in the case of a limited training set. During the test, the proposed U-Net model outperforms the Bayesian neural network (BNN) model for seismic stratum segmentation with regard to the training time, prediction speed, and segmentation accuracy. These results indicate the great potential of using U-Net-based deep learning for intelligent seismic stratigraphic interpretation.
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ISSN:1874-8961
1874-8953
DOI:10.1007/s11004-020-09916-8