Karst cave detection and prediction by using two fully convolutional neural networks-Reference-Cited by-同舟云学术

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Karst cave detection and prediction by using two fully convolutional neural networks

Published:2022-02-24 Issue: Volume: Page:
ISSN:
Container-title:SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021
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Author:

Yan Xingyu¹,Gu Hanming¹,Yan Youping²

Affiliation:

1. China University of Geosciences

2. Sinopec Engineering Geophysics Co., Ltd.

Publisher

Society of Exploration Geophysicists

Link

https://library.seg.org/doi/pdf/10.1190/iwmg2021-23.1

Reference10 articles.

1. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2. A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

3. Improving seismic fault detection by super-attribute-based classification

4. Identification for the fracture-cave in the Ordovician marine carbonate by using seismic diffraction inversion

5. Multiattribute fusion-based level sets for caves segmentation

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep carbonate fault–karst reservoir characterization by multi‐task learning;Geophysical Prospecting;2023-12-07

2. Elastic Full-Waveform Inversion Using a Physics-Guided Deep Convolutional Encoder–Decoder;IEEE Transactions on Geoscience and Remote Sensing;2023

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