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Spectral Analysis of Main Mineral Pigments in Thangka |
CEN Yi1, ZHANG Lin-shan1,2, SUN Xue-jian1*, ZHANG Li-fu1, LIN Hong-lei1, ZHAO Heng-qian3, WANG Xue-rui4 |
1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
3. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
4. Beijing Auspicious Earth Culture Communication Co., Ltd.,Beijing 100029, China |
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Abstract As a work of art, Thangka has both high historical value and artistic value. The identification and analysis of the mineral pigments of the Thangka are of great significance to the identification, repair, digital archiving and reproduction of the Thangka. This is the first systematic spectral analysis of five kinds of main Thangka mineral pigment, commonly used in the process. Through an in-depth analysis of the spectral characteristics of mineral pigments, we summarized the spectral features of Thangka’s main mineral pigments. By comparing the spectral characteristics of the same mineral pigment powder, blend bone glue and pigment on the pigment, we found that the reflectance of the powder pigment decreases after the blending of bone glue, and there are two strong absorption peaks near 1 447 and 1 928 nm. When you paint the glue soluble on the cloth, with the reduction of water paste in the paint, the two peaks become weaker, and the absorption peak at 1 447 nm or even disappears. Therefore, the spectra of mineral pigment powder and pigment on the cloth are very close. Mineral pigment powder can be directly used in the analysis of the Thangka pigment spectra match and analysis in the later period. Red mineral pigments on Thangka are cinnabar, whose mineral composition is HgS. The reflectance in the visible band rises after the first drop, and there is a deep absorption valley near 500 nm (430~530 nm). After the rapid rise of red, the reflectance curve near infrared changes slowly, and there are weak absorption valleys in the vicinity of 1 940 and 2 250 nm. There are three main types of Thangka yellow mineral pigments: desert tan(realgar, orpiment), ochre and gold, whose main components are arsenic sulfide, iron oxide and gold. Their spectral characteristics are concentrated in the visible spectrum between 400~500 nm, and the absorption valley position and absorption depth of different pigments are different. Near infrared reflectance Ochre is low, and the 860 nm has appeared near the absorption peak; while desert tan, realgar and orpiment in near infrared and shortwave infrared spectrum show high values and flat curves, with two weak absorptions in the valley near 1 890 and 2 230 nm. The absorption valley of gold in visible band is narrow and shallow, which can be used as the basis to distinguish it. Thangka’s blue mineral pigment is azurite, which has strong absorption characteristics in 500~1 000, 1 500, 2 040, 2 285 nm and near 2 350 nm, and weak absorption characteristics in 1 885 and 1 980 nm. Thangka’s green mineral pigment is malachite, and the spectrum has a strong broad absorption feature in 550~1 000, 2 270 and 2 350 nm. Although the main mineral compositions of malachite and azurite are both copper carbonate, but the reflectance value of malachite in 900~1 900 nm increases slowly, and there is no absorption characteristic at 1500nm, which can be used to distinguish them. Thangka’s white mineral pigments are mainly clay and clam, respectively, calcium carbonate and kaolin-clay. In the visible spectral range, clam has a weak absorption characteristic in 370 nm, and the clay has two obvious absorption characteristics in the 370nm and 730nm, which can be used to distinguish them. In the short wave infrared and near-infrared spectrum, clay has obvious absorption characteristics in 1 425, 1 930 and 2 230 nm, while clam has obvious absorption characteristics in 1 930 and 2 320 nm, plus a weak absorption characteristics in 1 440 nm. As for the same mineral pigment powder, the larger the mineral powder particle is, the darker the color of the pigment will be, and the lower the reflectance of the spectral characteristics will be.
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Received: 2018-02-05
Accepted: 2018-06-18
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Corresponding Authors:
SUN Xue-jian
E-mail: sunxj@radi.ac.cn
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[1] WANG Rui(王 瑞). The Collection and Appreciation of Thangka(唐卡的收藏与鉴赏). Beijing: The China Books Publishing Company(北京:中国书籍出版社), 2013.
[2] LIANG Jin-xing, WAN Xiao-xia(梁金星,万晓霞). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(8): 2519.
[3] Castro K, Vandenabeele P, Rodr Guez-laso M D. Analytical and Bioanalytical Chemistry, 2004, 379(4): 674.
[4] WANG Yu, ZHANG Xiao-tong, WU Na, et al(王 玉,张晓彤,吴 娜). The Journal of Light Scattering(光散射学报), 2017, 29(1): 39.
[5] SHI Ning-chang, LI Guang-hua, LEI Yong, et al(史宁昌,李广华,雷 勇,等). Sciences of Conservation and Archaeology(文物保护与考古科学), 2017, 29(3): 23.
[6] ZHONG Yan-fei, MA Ai-long, ONG Yew-soon, et al(钟燕飞,马爱龙,ONG Yew-soon,等). Applied Soft Computing, 2018, 64(3): 75.
[7] Jizao, Zhong Yanfei, Jia Tianyi, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 135(1): 31.
[8] Wang Xinyu, Zhong Yanfei, Zhang Liangpei, et al. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11): 6287.
[9] WU Feng-qiang, YANG Wu-nian, LI Dan(武锋强,杨武年,李 丹). Acta Mineralogica Sinica(矿物学报), 2014, 34(2): 166.
[10] GONG Meng-ting, FENG Ping-li(巩梦婷, 冯萍莉). Sciences of Conservation and Archaeology(文物保护与考古科学), 2014, 26(4): 76.
[11] WANG Le-le, LI Zhi-min, MA Qing-lin(王乐乐,李志敏,马清林,等). Dunhuang Research(敦煌研究), 2015, 3(1): 122.
[12] Zhang Lifu, Sun Xuejian, Wu Taixia, et al. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11): 2188.
[13] Sun Xuejian, Zhang Lifu, Yang Hang, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(5): 2198. |
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