Rock thin-section analysis and identification based on artificial intelligent technique-Reference-Cited by-同舟云学术

Rock thin-section analysis and identification based on artificial intelligent technique

Published:2022-08 Issue:4 Volume:19 Page:1605-1621
ISSN:1995-8226
Container-title:Petroleum Science
language:en
Short-container-title:Petroleum Science

Author:

Liu He,Ren Yi-Li,Li Xin,Hu Yan-Xu,Wu Jian-Ping,Li Bin,Luo Lu,Tao Zhi,Liu Xi,Liang Jia,Zhang Yun-Ying,An Xiao-Yu,Fang Wen-Kai

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Economic Geology,Geochemistry and Petrology,Geology,Geophysics,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference16 articles.

1. Image processing and machine learning approaches for petrographic thin section analysis (Russian)[C];Budennyy,2017

2. Quantitative digital petrography: full thin section quantification of pore space and grains[C];Buono,2019

3. Image segmentation of watershed ore based on morphological optimization[J];Cai;Sci. Technol. Eng.,2020

4. Automatic extraction of pores in thin section of rock casting based on deep learning[J];Cai;Sci. Technol. Eng.,2020

5. Rock fabric recognition based on image processing and neural network[J];Cheng;J. Xi'an Shiyou Univ. (Nat. Sci. Ed.),2013

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