| Zhengzhou Institute of Multipurpose Utilization of Mineral Resources, Chinese Academy of Geological Sciences | Host |
| Citation: |
ZHAO Yi, WANG Shoujing, GUO LiXiang, ZHU Likuan, LIU Lei. Index Calculation and Automatic Identification of Quartz Fluid Inclusions Based on Machine Vision[J]. Conservation and Utilization of Mineral Resources, 2022, 42(5): 28-34. doi: 10.13779/j.cnki.issn1001−0076.2022.05.005
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Index Calculation and Automatic Identification of Quartz Fluid Inclusions Based on Machine Vision
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Abstract
The content of fluid inclusions in quartz has a significant effect on the grade of quartz purification and the subsequent melting processing properties of quartz products. However, the traditional method of identifying quartz inclusions and qualitatively evaluating the content of fluid inclusions under artificial microscope has some defects in its efficiency and quantification accuracy. In this paper, the machine vision technology was used to segment and extract quartz particles from the micrographs of inclusions, and the self-developed quantitative calculation software of quartz fluid inclusions was used to carry out grayscale quantitative statistics on the characteristics of fluid inclusions inside quartz particles. In order to verify the reliability of the calculated results, this paper adopted the IOTA-STA standard sand as the reference sample and then established its sample data set. By the random sampling method, the sampling simulation condition for the stable existence of the reference standard value of the standard sample and the optimal number of image acquisition were obtained, and the calculation method for fluid inclusion index of the measured quartz sample was established. In addition, the fluid inclusion indexes of quartz samples from different sampling locations of pegmatite high-purity quartz LD-5 vein in the Eastern Qinling mountains of Henan Province were calculated. The results showed that the mean fluid inclusion index of LD-5 was 93.09,which was close to the Unimin standard sand and fluctuated slightly. Above all, this work provided useful reference and guidance for further optimization of prospecting target.
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Keywords:
- high purity quartz /
- machine vision /
- index of inclusion /
- screening and assessment
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References
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ZHAO Yi, WANG Shoujing, GUO LiXiang, ZHU Likuan, LIU Lei. Index Calculation and Automatic Identification of Quartz Fluid Inclusions Based on Machine Vision[J]. Conservation and Utilization of Mineral Resources, 2022, 42(5): 28-34. doi: 10.13779/j.cnki.issn1001−0076.2022.05.005
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Figure 1.
Polarizing picture (a) and transmittance picture (b) taken by quartz particles in the same field of view(Digital images captured by ZEISS Axioskop 40 microscope at 100× )
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Figure 2.
Schematic diagram of spatial information extraction process of quartz particle occupation by enhanced polarized light image
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Figure 3.
Schematic diagram of the synthesized image of the extracted quartz particles' spatial quantization inclusion
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Figure 4.
Cumulative density curve of quartz inclusions transparency probability
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Figure 5.
Software architecture diagram of automatic quartz inclusion identification and quantitative analysis system
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Figure 6.
The relationship between sampling rate and gray integral mean of Eunimin standard sand data set under different sampling times
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Figure 7.
Statistical box of gray integral value of inclusion of Unimin standard sand under different number of samples taken
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Figure 8.
Inclusion index test results of high purity quartz samples from the Eastern Qinling Mountains of Henan Province