基于机器学习的矿物智能识别方法研究进展与展望

郝慧珍; 顾庆; 胡修棉

doi:10.3799/dqkx.2020.360

基于机器学习的矿物智能识别方法研究进展与展望

doi: 10.3799/dqkx.2020.360

郝慧珍^{1, 2, ,},
顾庆^{1, 3},
胡修棉^4, ,

1.
南京大学软件学院, 江苏南京 210023
2.
南京工程学院信息与通信工程学院, 江苏南京 211167
3.
南京大学计算机科学与技术系, 江苏南京 210023
4.
南京大学地球科学与工程学院, 江苏南京 210023

基金项目:

国家自然科学基金项目 41972111

国家自然科科学技术部第二次青藏高原科学考察研究计划项目学基金项目 STEP 2019QZKK020405

详细信息

作者简介:
郝慧珍(1974-), 女, 讲师, 博士研究生, 研究方向为人工智能与地球科学交叉.ORCID: 0000-0002-4910-763X.E-mail: haohuizhen@njit.edu.cn

通讯作者:
胡修棉, E-mail: huxm@nju.edu.cn

中图分类号: P575
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- 被引次数: 0
出版历程
- 收稿日期: 2020-09-16
- 网络出版日期: 2021-10-14
- 刊出日期: 2021-10-14

Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning

Hao Huizhen^{1, 2
,
,},
Gu Qing^{1, 3},
Hu Xiumian^{4
, ,}

1.
Software Institute, Nanjing University, Nanjing 210023, China
2.
School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3.
Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
4.
School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China

摘要

摘要: 矿物智能识别是地球科学与信息科学的重要交叉方向，显示出强大的生命力.本文在调研国内外研究动态的基础上，把矿物智能识别划分为4个阶段，即矿物采集、数据获取、模型构建、分类判别；根据测试方法和获得的数据类型，把矿物智能识别分为基于化学成分、显微光学图片、光谱分析的3条基本路线；总结了应用于矿物智能识别的机器学习方法和技术，包括统计学习、线性回归模型、距离度量模型、树结构模型、神经网络模型及其与样本问题相关的新技术.在此基础上，提出消除地质学与人工智能的鸿沟、建设可用于学习的高质量矿物数据集、完善适合矿物智能识别的机器学习方法、增进模型可解释性、加强工业推广的实践是该领域未来的重点发展方向.
- 机器学习 /
- 矿物智能识别 /
- 矿物学 /
- 人工智能 /
- 地质学
Abstract: Mineral intelligent identification is a developing interdisciplinary research field between earth science and information science, where machine learning shows great vitality. This paper divides the procedure of mineral intelligent identification into four stages, including mineral collection, data acquisition, model building and category discriminant. Based on the test methods and data types, the mineral intelligent identification can be achieved by three different research routes, namely, chemical-composition-based, microscopic-optical-image-based and spectral-image-based. Various methods of machine learning for mineral intelligent recognition are reviewed in detail including statistical learning, similarity measurement, decision tree, artificial neural network and few new technologies related to testing sample. We suggest that the future directions in this field are to eliminate the gap between geology and artificial intelligence, to build high-quality mineral datasets that can be learned by the machine, to explore and consummate machine learning methods suitable for mineral intelligence identification, to increase the ability of model explanation, and to strengthen the practice of industrial application.
- machine learning /
- mineral identification /
- mineralogy /
- artificial intelligence /
- geology

HTML全文

图 1 智能矿物鉴定的阶段示意

a. 阶段1，获取矿物标本，主要途径有岩石手标本的矿物、单颗粒矿物、钻探标本的矿物等；b. 阶段2，使用仪器获取对矿物的描述数据，包括图像、图形、化学和物理数据；c. 阶段3，采用机器学习方法学习矿物识别的判别模型；d. 阶段4，采用模型对待识别数据进行判别获得矿物的类别

Fig. 1. A simplified carton showing four research stages of the mineral intelligent identification

下载: 全尺寸图片幻灯片

图 2 矿物智能识别的机器学习方法发展趋势

各方法的参考文献见表 2和正文描述

Fig. 2. The development trend of the various methods of machine learning applied in the mineral intelligent identification

下载: 全尺寸图片幻灯片

表 1 基于不同矿物数据集的矿物智能识别研究路线

Table 1. Summary of three research routes based on different datasets for the mineral intelligent identification

研究路线	数据类型	数据集优缺点	涉及的机器学习方法	主要参考文献
化学成分分析	EDS数据	数据获取快；化学元素数据准确度不高	查找表，最大似然法；	Ruisanchez et al., 1996
	SEM/EDS	空间分辨率高；数据获取快；元素准确度不高	神经网络，统计方法，多层感知神经网络技术，无监督神经网络Kohonen，决策树	Nielsen et al., 1998; Larsen et al., 2000; Flesche et al., 2000; Gallagher and Deacon, 2002; Akkaş et al., 2015
	EMPA数据	化学元素准确率高，数据获取费时费力，不能满足大规模测试需求	无监督神经网络Kohonen	Tsuji et al., 2010
	LIBS数据	数据获取快，要求测试矿物颗粒大(> 350 μm)；不适合自然界多数自然产出的矿物颗粒.	偏最小二乘回归(PLS)，多变量曲线分辨率‒交互最小二乘法(MCR-ALS)，最小绝对收缩和选择算子(Lasso)，判别函数分析(DFA)，多元曲线分辨‒交替最小二乘法(MCR-ALS)	Harmon et al., 2009; Clegg et al., 2009; Alvey et al., 2010; Dyar et al., 2012; Remus et al., 2012; Ali et al., 2016; Khajehzadeh et al., 2016; El Haddad et al., 2019
显微光学图片分析	显微图像	地质学最常见矿物判别的数据类型.目前数据集有限.最有前景的研究路线.	平行管道法，最大似然法和模糊分类技术，阈值法，决策树，神经网络，多层感知器神经网络，多尺度分割算法，统计方法	Launeau et al., 1994; Marschallinger, 1997; Marschallinger and Hofmann, 2010; Ross et al., 2001; Thompson et al., 2001; Holden et al., 2009; Baykan and Yilmaz, 2010; 叶润青等, 2011; Gomes et al., 2010, 2013; Aligholi et al., 2015, 2017; 赵启明等, 2015; Izadi et al., 2013, 2017; Ramil et al., 2018; Zhang et al., 2019; Borges and de Aguiar, 2019; Maitre et al., 2019
光谱分析	Raman	这是矿物种类鉴定最可靠的方法，也有国际数据集，但测试仪器昂贵，大面积推广存在困难	神经网络，主成分分析，偏最小二乘，加权邻居分类器，随机森林，卷积神经网络，深度神经网络，Siamese网络等	Ishikawa and Gulick, 2013a, 2013b; Lopez-Reyes et al., 2014; Carey et al., 2015; Liu et al., 2017, 2019; Sevetlidis and Pavlidis, 2019; Zhang et al., 2019

下载: 导出CSV

表 2 矿物智能识别的不同机器学习方法对比表

Table 2. Summary of various methods of machine learning applied in the mineral intelligent identification

分类模型	算法类型	主要优缺点	主要参考文献
统计判别方法	MLE, NB	简单迅速，适用于多分类，独立分布效果较好，需要的样本量也较少.	Marschallinger, 1997; Nielsen et al., 1998; Flesche et al.2000; Holden et al., 2009; Aligholi et al., 2015
线性回归模型	PLS, PLSDA, LASSO, ElasticNet	去除多重相关性，易于实现，只有很少的调优参数	Clegg et al., 2009; Alvey et al., 2010; Dyar et al.2012; Remus et al., 2012; Lopez-Reyes et al., 2014; Ali et al., 2016; Cochrane and Blacksberg, 2015; Khajehzadeh et al., 2016; El Haddad et al., 2019; Gibbons, 2020
距离度量模型	KNN	简洁，可扩展	Carey et al., 2015; Aligholi et al., 2017; Borges et al., 2019; Maitre et al., 2019
树结构模型	C4.5, CART, ID3, RF, ET	易于理解，要求与数据量少，可以处理数值型和类别型数据，具有较强的鲁棒性.	Ross et al., 2001; Ishikawa and Gulick, 2013b; Akkaş et al., 2015; Borges and de Aguiar, 2019; Maitre et al., 2019; Sevetlidis and Pavlidis, 2019; 赵永翼等, 2020
神经网络模型	SOM	结果容易理解，实现简单	Ruisanchez et al., 1996; Zupan et al., 1997; Tsuji et al., 2010
	MLPNN	可以拟合复杂模式，解决线性不可分问题	Thompson et al., 2001; Gallagher and Deacon, 2002; Baykan and Yilmaz, 2010; Ishikawa et al., 2013b; Izadi et al., 2013, 2017; Lopez-Reyes et al., 2014; 赵启明, 2015; Ramil et al., 2018
	DCNN, Net Inception-v3, Unet, ONN	可以表示复杂结构数据，实现端到端的学习	Liu et al., 2017; 徐述腾和周永章, 2018; Iglesias et al., 2019; Zhang et al., 2019; 彭伟航等, 2019; 郭艳军等, 2020; Li et al., 2020; 张旭等, 2020
样本问题新技术	迁移学习, 多任务学习	解决两个分类任务相互隔离的问题	Li et al., 2017; 李明超等, 2020; Li et al., 2020
样本问题新技术	Siamese网络	解决类不平衡和标注样本数量少的问题	Liu et al., 2019; 吴承炜等, 2020

下载: 导出CSV

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基于机器学习的矿物智能识别方法研究进展与展望

doi: 10.3799/dqkx.2020.360

作者简介:
郝慧珍(1974-), 女, 讲师, 博士研究生, 研究方向为人工智能与地球科学交叉.ORCID: 0000-0002-4910-763X.E-mail: haohuizhen@njit.edu.cn

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胡修棉, E-mail: huxm@nju.edu.cn

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Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning

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基于机器学习的矿物智能识别方法研究进展与展望

doi: 10.3799/dqkx.2020.360

作者简介: 郝慧珍(1974-), 女, 讲师, 博士研究生, 研究方向为人工智能与地球科学交叉.ORCID: 0000-0002-4910-763X.E-mail: haohuizhen@njit.edu.cn

通讯作者: 胡修棉, E-mail: huxm@nju.edu.cn

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Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning

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出版历程

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作者简介:
郝慧珍(1974-), 女, 讲师, 博士研究生, 研究方向为人工智能与地球科学交叉.ORCID: 0000-0002-4910-763X.E-mail: haohuizhen@njit.edu.cn

通讯作者:
胡修棉, E-mail: huxm@nju.edu.cn