Mapping Himalayan leucogranites by machine learning using multi-source data

doi:10.13745/j.esf.sf.2023.5.22

Abstract

Abstract:

Rare-metal elements are irreplaceable in the advanced materials, new energy and information technology industries, making them key strategic mineral resources in global competition. The N-E trending Himalayan leucogranite belt, over 1000 km long, with proven rare-metal resource potential, is expected to become an important rare-metal metallogenic belt in China. The identification of Himalayan leucogranites has mainly relied on geological field mapping; however, the mapping results have high uncertainty due to poor natural conditions, difficult working conditions and lack of detailed geological research—which has hindered rare-metal prospecting in this area. This paper investigates how to delineate the spatial distribution of Himalayan leucogranites by machine learning using geochemical, geophysical and remote sensing data. Results show that (1) regional geochemical, geophysical and remote sensing data provide significant information for leucogranite mapping in a variety of ways. (2) Multi-source data fusion captures the complementarity advantage of using various types of datasets and provides additional diagnostic information for leucogranite mapping. (3) Deep learning algorithms can effectively mine multi-source geoscience data and significantly improve the identification accuracy of leucogranites than traditional machine learning.

Key words: rare metals elements, Himalayan leucogranite, multisource data fusion, machine learning

CLC Number:

P59
P588.121

WANG Ziye, ZUO Renguang. Mapping Himalayan leucogranites by machine learning using multi-source data[J]. Earth Science Frontiers, 2023, 30(5): 216-226.

Figures/Tables 11

Fig.1 Distribution map of leucogranites in the Himalayas orogen. Adapted from [6].

Fig.2 Mapping Himalayan leucogranites using geochemical survey data. Adapted from [33].

Fig.3 Mapping Himalayan leucogranites by ASTER and Sentinel-2 remote sensing. Adapted from [36].

Fig.4 Spectral curves derived from ASTER image (a) and chemical composition graphs (b) for Himalayan leucogranites and Cambrian granite gneiss. Adapted from [39].

Fig.5 Flowchart of multi-source geological data fusion. Adapted from [41].

Fig.6 Fe2O3 concentration layer from grid survey (a) and ASTER false color composite image bands 3, 2, 1 (b) of the Cuonadong dome. Adapted from [39].

Fig.7 Composite image combining Fe2O3 concentration layer and ASTER image (spectral bands B1-B9). Adapted from [39].

Fig.8 Lithological classification of the Cuonadong dome by remote sensing. (a) Composite image combining ASTER and Sentinel-2 remote sensing images. (b) ASTER image alone (adapted from [37]).

Fig.9 Lithological classification of the Cuonadong dome using multi-source data combined with random forest metric learning. Adapted from [39].

Fig.10 Lithological classification of the Cuonadong dome using multi-source data combined with fully convolutional network. Adapted from [45].

Table 1 Quantitative performance evaluation of three lithological classification methods for each lithological unit of the Cuonadong dome. Adapted from [37,39,45].

岩性单元	ASTER影像+ 随机森林^[37]识别率	ASTER影像+地球化学数据+ 随机森林^[39]识别率	ASTER影像+地球化学+航磁数据+ 全卷积神经网络^[45]识别率
侏罗系砂岩、板岩	97.0%	98.1%	99.0%
下古生界大理岩	39.7%	70.0%	83.0%
三叠系砂岩、板岩	51.9%	91.3%	95.0%
寒武纪花岗质片麻岩	65.6%	85.5%	94.0%
古生代黑云母石英片岩	54.5%	80.77%	92.0%
第四纪地层	84.9%	91.2%	94.0%
喜马拉雅淡色花岗岩	75.3%	87.8%	96.0%
总分精度	85.75%	93.16%	96.0%

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