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Carbonate Lithology Identification wth Generative Adversarial Networks
作者: Takashi Nanjo , Satoru Tanaka
DOI:
10.2523/IPTC-20226-MS
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参考文章
(8)
1.
Robert J. Dunham,
Classification of Carbonate Rocks According to Depositional Textures
Mem.Amer.Assoc.Petrol.Geol..
,vol. 38, pp. 108- 121 ,(1962)
2.
Ilya Sutskever, Geoffrey E. Hinton, Alex Krizhevsky,
ImageNet Classification with Deep Convolutional Neural Networks
neural information processing systems.
,vol. 25, pp. 1097- 1105 ,(2012)
3.
Camila Martins Saporetti, Leonardo Goliatt da Fonseca, Egberto Pereira, Leonardo Costa de Oliveira,
Machine learning approaches for petrographic classification of carbonate-siliciclastic rocks using well logs and textural information
Journal of Applied Geophysics.
,vol. 155, pp. 217- 225 ,(2018) ,
10.1016/J.JAPPGEO.2018.06.012
4.
Naif Alqahtani, Ryan T. Armstrong, Peyman Mostaghimi,
Deep Learning Convolutional Neural Networks to Predict Porous Media Properties
SPE Asia Pacific Oil and Gas Conference and Exhibition.
,(2018) ,
10.2118/191906-MS
5.
Meng Du, Hongyi Yin, Xiaoyan Chen, Xinqiang Wang,
Oil-in-Water Two-Phase Flow Pattern Identification From Experimental Snapshots Using Convolutional Neural Network
IEEE Access.
,vol. 7, pp. 6219- 6225 ,(2019) ,
10.1109/ACCESS.2018.2888733
6.
Patrycja Michalowicz, Ghassan AlRegib, Yazeed Alaudah, Motaz Alfarraj,
A Machine Learning Benchmark for Facies Classification
arXiv: Image and Video Processing.
,(2019)
7.
Takashi Nanjo, Satoru Tanaka,
Carbonate Lithology Identification with Machine Learning
Abu Dhabi International Petroleum Exhibition & Conference.
,(2019) ,
10.2118/197255-MS
8.
Yu Zeng, Kebei Jiang, Jie Chen, None,
Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks
Proceedings of the 2019 3rd International Conference on Information System and Data Mining.
pp. 16- 20 ,(2019) ,
10.1145/3325917.3325926
来源期刊
information processing and trusted computing
International Petroleum Technology Conference
2020 年,
Volume: , Issue: ,
Page:
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