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Related Experiment Video

Updated: Feb 27, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

Classification of the Surrounding Rock Based on Image Processing Analysis and Transfer Learning.

Yanyun Fan^1,2, Jiaqi Zhu^2,3, Hua Luo^1,2

¹Yunnan Institute of Water and Hydropower Engineering Investigation, Design Co., Ltd., Kunming 650021, China.

Journal of Imaging

|February 26, 2026

Summary

This summary is machine-generated.

This study introduces fractal theory and transfer learning for objective tunnel surrounding rock classification. New methods improve accuracy and support standardized engineering practices.

Keywords:

box dimension method feature extraction image processing analysis surrounding rock classification transfer learning

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Area of Science:

Geotechnical Engineering
Image Analysis
Fractal Geometry

Background:

Current surrounding rock classification relies heavily on subjective technician judgment, leading to inconsistent results.
Standardized methods for surrounding rock classification are insufficient, impacting tunnel safety and construction.
Existing methods lack objectivity and struggle with the complex spatial distribution of geological features.

Purpose of the Study:

To develop objective and accurate methods for surrounding rock classification using image analysis and transfer learning.
To introduce fractal theory for quantifying the complexity of surrounding rock surfaces.
To propose a strength correction method for surrounding rock based on image analysis and rebound meter data.

Main Methods:

Image processing and analysis of surrounding rock in a water diversion project tunnel.
Application of fractal theory to analyze the spatial distribution of geological features (weak layers, joints).
Development and application of a transfer learning model for rapid surrounding rock image classification.
Integration of quantified image parameters and rebound meter data for strength correction.

Main Results:

Fractal dimension analysis effectively revealed the complexity and irregularity of surrounding rock surfaces.
A novel classification method based on fractal theory was proposed and validated.
Image-based strength correction significantly reduced errors from uneven rock mass distribution.
A transfer learning model achieved rapid and accurate classification of tunnel surrounding rock images.

Conclusions:

The proposed methods, incorporating fractal theory and transfer learning, offer objective and standardized approaches to surrounding rock classification.
Image analysis combined with traditional methods enhances the accuracy of surrounding rock strength assessment.
This research provides crucial support for improving safety and efficiency in tunnel engineering through standardized surrounding rock classification.