基于深度学习的花岗伟晶岩型锂铍矿物识别研究

doi:10.13745/j.esf.sf.2023.5.20

地学前缘 ›› 2023, Vol. 30 ›› Issue (5): 185-196.DOI: 10.13745/j.esf.sf.2023.5.20

• 伟晶岩含矿性识别标志 • 上一篇下一篇

基于深度学习的花岗伟晶岩型锂铍矿物识别研究

蒋果²^,³^,⁴^,⁵(), 周可法¹^,⁵^,^*(), 王金林¹^,²^,³^,⁴^,⁵, 白泳¹^,³^,⁴, 孙国庆⁵^,⁶, 汪玮¹^,²^,³^,⁴^,⁵

1.中国科学院空间应用工程与技术中心, 北京 100049
2.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室, 新疆乌鲁木齐 830011
3.新疆矿产资源与数字地质重点实验室, 新疆乌鲁木齐 830011
⁴ 中国科学院新疆矿产资源研究中心, 新疆乌鲁木齐 830011
⁵ 中国科学院大学, 北京 100049
⁶ 中国科学院空天信息创新研究院, 北京 100190

收稿日期:2022-12-10 修回日期:2022-12-31 出版日期:2023-09-25 发布日期:2023-10-20
通讯作者: 周可法
作者简介:蒋果(1994—),男,助理研究员,主要从事高光谱遥感地质应用工作。E-mail: jguo@ms.xjb.ac.cn
基金资助:
中国科学院重点领域部署项目(ZDRW-ZS-2020-4-2);国家科学技术部新疆第三次科学考察项目(2022xjkk1306);国家自然科学基金新疆联合基金项目(U2003107)

Identification of lithium-beryllium granitic pegmatites based on deep learning

JIANG Guo²^,³^,⁴^,⁵(), ZHOU Kefa¹^,⁵^,^*(), WANG Jinlin¹^,²^,³^,⁴^,⁵, BAI Yong¹^,³^,⁴, SUN Guoqing⁵^,⁶, WANG Wei¹^,²^,³^,⁴^,⁵

1. Center for Space Application Engineering and Technology, Chinese Academy of Sciences, Beijing 100094, China
2. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, ürümqi 830011, China
3. Xinjiang Key Laboratory of Mineral Resources and Digital Geology, ürümqi 830011, China
4. Xinjiang Research Centre for Mineral Resources, Chinese Academy of Sciences, ürümqi 830011, China
5. University of Chinese Academy of Sciences, Beijing 100049, China
6. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Received:2022-12-10 Revised:2022-12-31 Online:2023-09-25 Published:2023-10-20
Contact: ZHOU Kefa

摘要/Abstract

摘要：

虽然遥感技术在大宗型金属矿产资源勘查方面取得了非常卓越的成效,但将其应用于稀有金属矿物提取的成果较少,尤其是对硬岩型锂铍矿物识别,主要受光谱分辨率、含矿岩体与围岩物性差异小、锂铍矿物光谱区分差异小等因素限制。为此,本研究通过野外采集含锂铍矿物伟晶岩和围岩样品并测量其光谱数据,使用光谱增强技术凸显光谱特征,对比分析特征吸收参数相似度模型和深度神经网络模型对矿物识别精度的影响。结果表明:(1) 结合包络线去除和混合高斯模型提取的光谱吸收特征参数更简洁且具有更强的地质内涵;(2)光谱增强技术可提高模型识别精度,对比原始光谱,基于对数一阶导数光谱构建的模型的总体精度提高了0.05;(3) 从总体精度看,深度卷积神经网络(总体精度=0.78)比浅层网络模型(反向传播模型总体精度=0.55和极限学习机模型总体精度=0.73)能够取得更好的效果。因此,结合高光谱技术和深度学习能够有效快速地识别花岗伟晶岩型矿物,为航空-航天成像高光谱影像直接提取锂铍矿物提供科学依据。

关键词: 锂、铍, 光谱变换, MICA, 深度学习, 高光谱

Abstract:

Although remote sensing technology is widely used in large-scale exploration of metallic mineral resources, its application in direct rare-metal identification is limited, especially in the identification of hard rock Li/Be-bearing minerals. The problem is mainly due to low spectral resolution, low spatial resolution due to high physical similarity between ore body and wallrock, and small spectral difference between Li/Be-bearing minerals. To address this issue we investigate mineral identification methods based on deep learning models. Samples of Li-Be pegmatites and wallrock are collected from several pegmatite deposits and relevant spectral data are obtained. Spectral enhancement techniques are used to highlight the characteristic spectral features, and the characteristic absorption band similarity model and deep neural network models are compared for mineral identification accuracy. Results show that (1) the extracted characteristic absorption bands using a combination of envelope removal and mixed Gaussian model are better defined and reveal more geological insight. (2) Appropriate spectral enhancement can improve the accuracy of spectral models. In the case studied, the overall accuracy of the spectral model increases by 0.05 based on the logarithmic-first-order derivative spectrum over the original spectrum. (3) In terms of overall model accuracy, deep convolutional neural networks (0.78) perform better than shallow neural networks (0.55 for backpropagation; 0.73 for Extreme Learning Machines). Overall, the combination of hyperspectral imaging and deep convolutional neural network model can quickly and effectively identify pegmatite-hosted minerals, which offer a scientific basis for the direct identification of Li/Be-bearing minerals by satellite remote sensing.

Key words: lithium-beryllium, spectral transformation, MICA, deep learning, hyperspectral

中图分类号:

蒋果, 周可法, 王金林, 白泳, 孙国庆, 汪玮. 基于深度学习的花岗伟晶岩型锂铍矿物识别研究[J]. 地学前缘, 2023, 30(5): 185-196.

JIANG Guo, ZHOU Kefa, WANG Jinlin, BAI Yong, SUN Guoqing, WANG Wei. Identification of lithium-beryllium granitic pegmatites based on deep learning[J]. Earth Science Frontiers, 2023, 30(5): 185-196.

图/表 9

图1 训练样本示意图 a—光谱曲线图;b—光谱曲线二值图。

Fig.1 Training data example. (a) Spectral curve. (b) Spectral curve in binary form

图2 部分样品 a—锂辉石伟晶岩;b—锂云母伟晶岩;c—锂电气石伟晶岩;d—绿柱石伟晶岩。

Fig.2 Representative rock specimens. (a) Spodumene pegmatite; (b) lepidolite pegmatite; (c) Li-bearing tourmaline pegmatite; (d) beryl pegmatite.

图3 光谱拟合吸收曲线及相关系数 a—锂辉石;b—锂电气石;c—锂云母;d—绿柱石;a1-d1—拟合曲线;a2-d2—回归系数。

Fig.3 Spectral model fit with absorption curve (left panel) and model fitting quality evaluation (right panel). (a1, a2) Spodumene; (b1, b2) Li-rich tourmaline; (c1, c2) lepidolite; (d1, d2) beryl.

图4 锂矿物光谱 a—反射率光谱;b—包络线去除光谱。

Fig.4 Reflectance spectra of spodumene (black), Li-rich tourmaline (blue) and lepidolite (red) before (a) and after (b) spectral enhancement by envelope-removal technique

图5 绿柱石矿物光谱 a—反射率光谱;b—包络线去除光谱。

Fig.5 Reflectance spectra of beryl before (a) and after (b) envelop-removal enhancement

图6 锂铍矿物光谱吸收特征参数 a—锂辉石;b—锂云母;c—锂电气石;d—绿柱石。

Fig.6 Characteristic absorption-band parameters for (a) spodumene, (b) lepidolite, (c) Li-rich tourmaline and (d) beryl

图7 不同变换光谱

Fig.7 Spectral transformation results by different techniques

表1 不同变换光谱下ELM模型识别精度

Table 1 Evaluation of ELM model accuracy under different spectral transformations

变换形式	神经元个数	训练精度	验证精度	测试精度
R	52	0.913	0.821	0.675
e^R	53	0.923	0.829	0.683
$R$	41	0.916	0.821	0.671
1/R	74	0.891	0.807	0.638
CR(R)	54	0.891	0.829	0.707
lnR	55	0.927	0.829	0.711
R'	57	0.886	0.843	0.719
R″	55	0.848	0.743	0.512
$1 R$ '	56	0.784	0.693	0.598
(lnR)'	78	0.927	0.857	0.728
(e^R)'	55	0.895	0.843	0.715
( $R$ )'	42	0.881	0.836	0.711
$1 R$ ″	56	0.843	0.721	0.451
(lnR)″	53	0.845	0.779	0.61
(e^R)″	54	0.857	0.743	0.541
( $R$ )″	64	0.861	0.736	0.533

表1 不同变换光谱下ELM模型识别精度

Table 1 Evaluation of ELM model accuracy under different spectral transformations

变换形式	神经元个数	训练精度	验证精度	测试精度
R	52	0.913	0.821	0.675
e^R	53	0.923	0.829	0.683
$R$	41	0.916	0.821	0.671
1/R	74	0.891	0.807	0.638
CR(R)	54	0.891	0.829	0.707
lnR	55	0.927	0.829	0.711
R'	57	0.886	0.843	0.719
R″	55	0.848	0.743	0.512
$1 R$ '	56	0.784	0.693	0.598
(lnR)'	78	0.927	0.857	0.728
(e^R)'	55	0.895	0.843	0.715
( $R$ )'	42	0.881	0.836	0.711
$1 R$ ″	56	0.843	0.721	0.451
(lnR)″	53	0.845	0.779	0.61
(e^R)″	54	0.857	0.743	0.541
( $R$ )″	64	0.861	0.736	0.533

表2 不同方法精度比较

Table 2 Comparison of overall identification accuracy of different methods

方法	训练集精度	验证集精度	测试集精度
方法	训练集精度	验证集精度	R²	Kappa系数
MICA	0.719	0.637	0.496	0.431
BP	0.774	0.693	0.549	0.415
ELM	0.927	0.857	0.728	0.651
DCNN	0.966	0.882	0.776	0.716

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基于深度学习的花岗伟晶岩型锂铍矿物识别研究

Identification of lithium-beryllium granitic pegmatites based on deep learning

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