Reservoir Lithology Identification Based on Improved Adversarial Learning-Reference-Cited by-同舟云学术

Reservoir Lithology Identification Based on Improved Adversarial Learning

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Song Lei¹^ORCID,Yin Xingyao¹^ORCID,Yin Linjie¹

Affiliation:

1. School of Geosciences, China University of Petroleum (East China), Qingdao, China

Funder

Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology

National Natural Science Foundation of China

Innovation Fund Project for Graduate Student of China University of Petroleum (East China) and Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10138893.pdf?arnumber=10138893

Reference20 articles.

1. Seismic Lithology/Fluid Prediction via a Hybrid ISD-CNN

2. Evaluation of machine learning methods for lithology classification using geophysical data

3. Semi-Supervised Learning Based on Generative Adversarial Network and Its Applied to Lithology Recognition

4. Bayesian Convolutional Neural Networks for Seismic Facies Classification

5. Seismic inversion with deep learning

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