Key Takeaways & Executive Findings
- •• Deep learning models (PSPNet, U-Net, FPN, LinkNet) automate quartz identification in thin sections, reducing manual effort and expertise requirements. • PSPNet achieved the highest IoU scores, demonstrating superior segmentation accuracy even in complex mineral coexistence scenarios. • The study used a comprehensive dataset of 2470 hyperspectral images from 120 thin sections, with expert-reviewed masks ensuring robust training. • This automated approach enhances reliability and efficiency, offering a valuable tool for geologists and advancing mineralogical analysis.
Abstract
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance. Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise, often complicated by the coexistence of other minerals. This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals. The utilized four advanced deep learning models—PSPNet, U-Net, FPN, and LinkNet—has significant advancements in efficiency and accuracy. Among these models, PSPNet exhibited superior performance, achieving the highest intersection over union (IoU) scores and demonstrating exceptional reliability in segmenting quartz minerals, even in complex scenarios. The study involved a comprehensive dataset of 120 thin sections, encompassing 2470 hyperspectral images prepared from 20 rock samples. Expert-reviewed masks were used for model training, ensuring robust segmentation results. This automated approach not only expedites the recognition process but also enhances reliability, providing a valuable tool for geologists and advancing the field of mineralogical analysis.
1. Introduction
The recognition of quartz minerals holds great significance in the fields of mineralogy and geology. Quartz, a widespread mineral in our natural environment and industrial applications, demands precise identification due to its prevalence [1–2]. Quartz possesses specific optical characteristics, yet it frequently coexists with other minerals, complicating the identification process. These challenges become apparent during the examination of thin sections, causing significant difficulties for geologists [3–4].
The mineralogical analysis of thin sections is a commonly employed method in geological studies [5–6]. Thin sections are used to examine the structure of rocks and minerals. However, these analyses are often time-consuming and require expertise [6–10]. Geologists must individually inspect each part of these thin sections and identify the minerals. This process involves gathering a substantial amount of data and interpreting it, demanding time [11–12].
Quartz identification using traditional optical mineralogy, such as polarizing microscopy, has been widely used. This method requires expert interpretation of optical properties like birefringence and extinction angles. However, such manual approaches are time-consuming and prone to human error, especially in heterogeneous rock samples [3,9]. The presented method using deep learning automates the entire identification process. Deep learning, especially semantic segmentation models, such as PSPNet and U-Net, significantly reduces the time and effort required, providing more consistent and accurate results [8,13]. The automation helps minimize the challenges faced by traditional methods in distinguishing quartz from other minerals, especially when these minerals coexist.
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Gökhan Külekçi, Kemal Hacıefendioğlu, Hasan Basri Başağa (2025). Enhancing mineral processing with deep learning: Automated quartz identification using thin section images. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-024-3048-8
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Frequently Asked Questions
What is the main contribution of this study?
The study introduces a deep learning-based approach for automated quartz identification in thin sections using hyperspectral imaging, achieving high accuracy and efficiency compared to traditional methods.
Which deep learning model performed best?
PSPNet outperformed U-Net, FPN, and LinkNet, achieving the highest Intersection over Union (IoU) scores for quartz segmentation.
What dataset was used in the study?
The dataset comprised 120 thin sections and 2470 hyperspectral images prepared from 20 rock samples, with expert-reviewed masks for training.
How does this method benefit geologists?
It reduces the time and expertise required for mineral identification, minimizes human error, and provides consistent, reliable results even in complex mineral assemblages.
What are the limitations of traditional quartz identification methods?
Traditional methods like polarizing microscopy are labor-intensive, require expert interpretation, and are prone to errors, especially when quartz coexists with other minerals.
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