Intelligent lithology identification via spectral-image fusion

Abstract

Lithology identification is of vital significance for fundamental geological research and engineering applications. Traditional methods rely on rock image features and often cause confusion among visually similar rocks. To enhance identification accuracy, spectral features are integrated to better represent rock composition. Nonetheless, spectral testing inevitably damages samples and is prone to challenges of the occurrence of similar spectra for different materials. This study explores the advantages of hyperspectral imaging technology, enabling the integration of spectral and image data without damage or contact. A novel spectral-image fusion method is proposed for lithology identification. It uses a dual-channel residual neural network that combines spectral and texture feature channels. Additionally, key features are effectively captured by spectral-spatial attention mechanisms. Finally, a customized transfer learning approach is utilized to fine-tune the pre-trained network on a small dataset for lithology identification at the tunnel site, facilitating rapid model adaptation. The research indicates that employing the ResNetX2-50 network for integrating spectral-image information yields optimal identification results, with a fusion accuracy of over 99% on the test set, which is 2 percentage points higher than the accuracy of a single spectral model and about 20 percentage points higher than the accuracy of a single texture model. Research findings provide robust technical support for non-destructive, non-contact, fast lithology identification in field investigations and construction projects.

Keywords

Spectral-image feature fusion / Spectral-spatial attention / Feature level fusion / Intelligent identification of lithology / Transfer learning

Data availability

The data and code that support the findings of this study are available on https://github.com/haha1583091/Spectral-Image-fusion-method-for-lithology-identification.

CRediT authorship contribution statement

Zhenhao Xu: Funding acquisition, Writing - review & editing, Conceptualization, Methodology, Supervision. Shan Li: Visualization, Formal analysis, Writing - original draft, Data curation. Peng Lin: Validation, Funding acquisition. Qianji Li: Validation, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

We would like to acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 52379103 and 52279103) and the Natural Science Foundation of Shandong Province (Grant No. ZR2023YQ049).

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