Research on rock sample lithology identification algorithm based on ResNet self-supervised learning

Dongxing Zhao; Han Wang; Wei He; Kun Ding; Haiyan Zhang

doi:10.1117/12.2626531

21 December 2021 Research on rock sample lithology identification algorithm based on ResNet self-supervised learning

Dongxing Zhao, Han Wang, Wei He, Kun Ding, Haiyan Zhang

Author Affiliations +

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560I (2021) https://doi.org/10.1117/12.2626531
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

The intelligent recognition of rock sample lithology plays an important role in mineral resources exploration. According to the rock sample image, a depth learning model is established. In order to solve the problem of gradient disappearance caused by the excessive depth of neural network, residual structure is introduced, the ResNet structure model is built, and a comparison based self-supervised learning classification algorithm is established, which does not depend on any label value. Using the encoder network to extract features and calculate the reconstruction error in pixel space, we can obtain the ability to identify new samples. The self-supervised lithology recognition algorithm takes resnet18 as the encoder network and the public ImageNet data set as the pre-training data. The parameters are optimized by using the comparative learning gradient descent of positive and negative samples, it adopts a linear classifier, The classification accuracy of rock samples is 85%, which is higher than the classification algorithm based on resnet18 and migration learning, and provides a scientific basis for lithology identification.

Citation Download Citation

Dongxing Zhao, Han Wang, Wei He, Kun Ding, and Haiyan Zhang "Research on rock sample lithology identification algorithm based on ResNet self-supervised learning", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560I (21 December 2021); https://doi.org/10.1117/12.2626531

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Computer programming

Convolution

Statistical modeling

Image enhancement

Image classification

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years