Quantitative Identification of Detrital Minerals by Mineral Characteristic Automatic Analysis System and Error Analysis with Traditional Microscopic Identification
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摘要:
碎屑矿物分析被广泛应用于沉积物物源和物质扩散研究中，对分析沉积动力环境和海洋动力特征方面具有重要意义，然而长期以来碎屑矿物数据的获取主要以光学显微镜为工具，依靠人工鉴定来完成，工作量大、效率低。为使科研人员及时获得科学有效的矿物鉴定数据，本文基于热场发射扫描电镜-X射线能谱仪，利用矿物特征自动定量分析系统(简称AMICS)，运用矿物表面微形貌观察和化学成分分析技术，通过实测数据自主建立的一套碎屑矿物标准库为分类依据，实现了对碎屑矿物的定量识别。AMICS系统对第一个样品共识别出矿物种类25种，人工实体显微镜-偏光显微镜法鉴定出25种；AMICS系统对第二个样品共识别出矿物种类26种，人工实体显微镜-偏光显微镜法鉴定出27种，两种方法鉴定出的碎屑矿物种类基本相同，且每一种矿物含量的误差绝对值均小于5%。该系统识别氧化物(褐铁矿、铬铁矿等)，磷酸盐(磷灰石等)，硫酸盐(重晶石等)，硫化物(黄铁矿等)，碳酸盐(方解石、白云石等)，部分硅酸盐(锆石、榍石、橄榄石、石英、钾长石、钠长石、石榴石族等)相对准确，但仅依靠矿物化学成分很难准确识别同质多象和类质同象系列碎屑矿物，层状硅酸盐矿物在制样时容易逐层脱落的问题需要进一步解决。
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关键词:
- 矿物特征自动定量分析系统(AMICS) /
- 背散射法 /
- 碎屑矿物 /
- 扫描电镜-能谱仪
Abstract:The analysis of detrital minerals is widely used in the study of sediment sources and material diffusion, and is of great significance in analyzing sedimentary dynamic environment and oceanic dynamic characteristics. However, for a long time, the acquisition of detrital mineral data has relied mainly on optical microscopes as tools and manual identification, which is labor-intensive and inefficient. In order to obtain scientific and effective mineral identification data in a timely fashion, a thermal field emission scanning electron microscopy with energy dispersive spectroscopy attached and an automated mineral identification and characterization system (AMICS) were used. For the first sample, 25 mineral species were identified by the AMICS system and 25 mineral species were identified by artificial identification with stereomicroscope and polarizing microscope. For the second sample, 26 mineral species were identified by the AMICS system, and 27 mineral species were identified by artificial identification. The two methods identified similar types of detrital minerals, and the absolute error of each mineral content was less than 5%. The AMICS system can be used to identify oxides (limonite, chromite, etc.), phosphates (apatite, etc.), sulfates (barite, etc.), sulfides (pyrite, etc.), carbonates (calcite, dolomite, etc.), and some silicates (zircon, titanite, olivine, quartz, potassium feldspar, sodium feldspar, garnet group, etc.) accurately but it is difficult to accurately identify polymorphic and isomorphic detrital minerals based solely on mineral chemical composition. The problem of layered silicate minerals easily falling off layer by layer during sample preparation needs to be solved.
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图 1 灰度值确定矿物物相并进行能谱谱图采集
Figure 1. Determination of mineral phases using grayscale values and collection of energy dispersive spectrum images
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图 3 部分同质多象矿物显微照片
（a）板钛矿；（b）金红石；（c）锐钛矿；（d）蓝晶石；（e）矽线石；（f）偏光镜下矽线石。
Figure 3. Microscopic photos of partially polymorphic minerals. (a) Brookite; (b) Rutile; (c) Anatase; (d) Kyanite; (e) Sillimanite; (f) Sillimanite under polarizing microscope
表 1 沉积物样品处理结果
Table 1 Processing results of sediment samples
参数 1号样品前处理结果(g) 2号样品前处理结果(g) 沉积物质量 79.955 74.977 粒级质量 33.875 52.312 缩分 7.368 6.194 重矿物质量 0.371 0.303 
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表 2 重矿物检测结果统计
Table 2 Statistics of testing results for heavy minerals
矿物种类 1号样品检测结果(%) 2号样品检测结果(%) 实体显微镜-偏光显微镜法 X射线能谱-背散射法 误差 实体显微镜-偏光显微镜法 X射线能谱-背散射法 误差 磁铁矿 0.78 1.66 −0.88 偶见 0.90 −0.90 钛铁矿 8.31 4.28 4.03 7.48 5.89 1.59 赤、褐铁矿 2.60 1.88 0.72 0.70 0.83 −0.13 白钛石 1.56 0.53 1.03 1.81 0.35 1.46 锆石 0.52 0.11 0.41 1.04 0.36 0.68 独居石 − − − − − − 榍石 0.52 3.56 −3.04 1.29 4.08 −2.79 磷灰石 1.56 2.41 −0.85 1.41 1.82 −0.41 金红石 − − − 偶见 0.29 −0.29 电气石 0.52 0.49 0.03 0.47 0.06 0.41 石榴石 5.71 5.32 0.39 4.02 4.23 −0.21 斜方辉石 0.26 0.01 0.25 − − − 普通辉石 0.78 2.15 −1.37 2.06 4.85 −2.79 透辉石 0.52 4.25 −3.73 0.25 0.17 0.08 普通角闪石 23.38 21.15 2.23 25.40 28.26 −2.86 阳起石 2.60 5.19 −2.59 3.12 2.99 0.13 透闪石 0.52 1.33 −0.81 1.78 0.50 1.28 蓝闪石 0.52 2.30 −1.78 0.98 2.63 −1.65 绿帘石 25.97 24.56 1.41 26.67 23.73 2.94 (斜)黝帘石 3.90 1.42 2.48 4.30 2.17 2.13 十字石 − − − − − − 蓝晶石 − − − 0.27 0.16 0.11 红柱石 0.26 0.48 −0.22 0.23 0.17 0.06 矽线石 − − − − − − 黄玉 − − − − − − 硬绿泥石 − − − 0.54 − 0.54 绿泥石 偶见 1.12 −1.12 偶见 1.78 −1.78 黑云母 0.26 0.12 0.14 偶见 0.53 −0.53 白云母 0.52 0.19 0.33 0.66 0.06 0.6 风化云母 偶见 0.01 −0.01 0.88 0.01 0.87 自生黄铁矿 − − − − − − 碳酸盐 4.94 0.28 4.66 2.40 0.24 2.16 未知矿物 13.51 15.19 −1.68 12.22 12.96 −0.74
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