Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China

Abstract Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Huihui Cai, Siqiong Chen, Yongyang Xu, Zixuan Li, Xiangjin Ran, Xingping Wen, Yongsheng Li, Yanqing Men
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	Astronomy QB1-991 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/40d3868f96c14616b7d04b682724494e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:40d3868f96c14616b7d04b682724494e
record_format	dspace
spelling	oai:doaj.org-article:40d3868f96c14616b7d04b682724494e2021-11-23T21:03:08ZIntelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China2333-508410.1029/2021EA001927https://doaj.org/article/40d3868f96c14616b7d04b682724494e2021-11-01T00:00:00Zhttps://doi.org/10.1029/2021EA001927https://doaj.org/toc/2333-5084Abstract Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys.Huihui CaiSiqiong ChenYongyang XuZixuan LiXiangjin RanXingping WenYongsheng LiYanqing MenAmerican Geophysical Union (AGU)articleAstronomyQB1-991GeologyQE1-996.5ENEarth and Space Science, Vol 8, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astronomy QB1-991 Geology QE1-996.5
spellingShingle	Astronomy QB1-991 Geology QE1-996.5 Huihui Cai Siqiong Chen Yongyang Xu Zixuan Li Xiangjin Ran Xingping Wen Yongsheng Li Yanqing Men Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China
description	Abstract Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys.
format	article
author	Huihui Cai Siqiong Chen Yongyang Xu Zixuan Li Xiangjin Ran Xingping Wen Yongsheng Li Yanqing Men
author_facet	Huihui Cai Siqiong Chen Yongyang Xu Zixuan Li Xiangjin Ran Xingping Wen Yongsheng Li Yanqing Men
author_sort	Huihui Cai
title	Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China
title_short	Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China
title_full	Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China
title_fullStr	Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China
title_full_unstemmed	Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China
title_sort	intelligent recognition of ore‐forming anomalies based on multisource data fusion: a case study of the daqiao mining area, gansu province, china
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/40d3868f96c14616b7d04b682724494e
work_keys_str_mv	AT huihuicai intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT siqiongchen intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT yongyangxu intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT zixuanli intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT xiangjinran intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT xingpingwen intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT yongshengli intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina AT yanqingmen intelligentrecognitionoforeforminganomaliesbasedonmultisourcedatafusionacasestudyofthedaqiaominingareagansuprovincechina
_version_	1718416088707891200

Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China

Ejemplares similares