干旱区生态环境知识资源中心(Arid): Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

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DOI	10.3390/min13060766
	Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco
	Hajaj, Soufiane; El Harti, Abderrazak; Jellouli, Amine; Pour, Amin Beiranvand; Himyari, Saloua Mnissar; Hamzaoui, Abderrazak; Hashim, Mazlan
通讯作者	Hajaj, S
来源期刊	MINERALS
EISSN	2075-163X
出版年	2023
卷号	13 期号:6
英文摘要	Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth's surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and effective mapping of various types of lithologies in arid and semi-arid regions. This article discusses the use of machine learning algorithms, specifically Support Vector Machines (SVM), one-dimensional Convolutional Neural Network (1D-CNN), random forest (RF), and k-nearest neighbor (KNN), for lithological mapping in a complex area with strong hydrothermal alteration. The study evaluates the performance of the four algorithms in three different zones in the Ameln valley shear zone (AVSZ) area at eastern Kerdous inlier, Moroccan western Anti-Atlas. The results demonstrated that 1D-CNN achieved the best classification results for most lithological units. Additionally, the LK-SVM demonstrated good mapping results compared to the other SVM models, as well as RF and KNN. Our study concludes that the combination of the CNN and HyMap data can provide the most accurate lithologic mapping for the three selected region, with an overall accuracy of similar to 95%. However, this study highlights the challenges in identifying different lithological units using remotely sensed data due to spectrum similarities induced by similar chemical and mineralogical compositions. This study emphasizes the importance of carefully considering and evaluating ML and DL methods for lithological mapping studies, then recommends the high-resolution hyperspectral data and DL models for accurate results. The implications of this study would be fascinating to exploration geologists for Mineral Prospectivity Mapping (MPM), especially in selecting the most appropriate techniques for highly accurate mineral mapping in metallogenic provinces.
英文关键词	HyMap imagery lithological mapping machine learning CNN KNN RF SVM
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001017451900001
WOS关键词	SUPPORT VECTOR MACHINE ; CENTRAL EAST GREENLAND ; CLASSIFICATION ; ALGORITHMS ; ACCURACY
WOS类目	Geochemistry & Geophysics ; Mineralogy ; Mining & Mineral Processing
WOS研究方向	Geochemistry & Geophysics ; Mineralogy ; Mining & Mineral Processing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397861
推荐引用方式 GB/T 7714	Hajaj, Soufiane,El Harti, Abderrazak,Jellouli, Amine,et al. Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco[J],2023,13(6).
APA	Hajaj, Soufiane.,El Harti, Abderrazak.,Jellouli, Amine.,Pour, Amin Beiranvand.,Himyari, Saloua Mnissar.,...&Hashim, Mazlan.(2023).Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco.MINERALS,13(6).
MLA	Hajaj, Soufiane,et al."Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco".MINERALS 13.6(2023).