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Previous studies have conducted extensive geological, geophysical, geochemical, remote sensing, and mineral resource surveys in thesouthern Great Xing'an Range, particularly in the Chifeng area. However, there is still a lack of effective prospecting methods for shallow overburdenareas. Considering the metallogenic geological background and spatial distribution patterns of deposits in this region, this study employed multi-sourceremote sensing technology to extract mineralization alteration in Hexigten Banner and Linxi County, northern Chifeng. Multispectral data (Landsat-8,ASTER) were used to extract alteration of different mineral types through principal component analysis. For GF-5 data, the mixed-tuned matchedfiltering technique was applied to extract mineralization alteration. The results from the three remote sensing methods were then integrated forcomprehensive analysis to delineate prospective metallogenic targets. We also conducted field verification, further advancing the application of remote sensing in geological and mineral resource exploration. Based on Landsat-8 and ASTER multispectral data, iron-stained and hydroxyl-bearing (Mg-OH, Al-OH) mineral information was extracted. Three alteration minerals of muscovite, epidote, and kaolinite were identified based on the GF-5 data.Field verification confirmed that the remote sensing extraction results corresponded well with actual alteration anomalies, and tin-copper-silver-leadzincmineralization was discovered within the delineated target areas. This study, by combining extraction and overlay results from different data sourcesand validating them through field surveys, demonstrates the accuracy of the remote sensing methods used for mineralization alteration extraction. Theintegration of multispectral and hyperspectral remote sensing data facilitates subsequent alteration zoning analysis and more precise metallogenicprediction, thereby better serving mineral resource exploration and related fields., correspAuthors=null, authorNote=20250202.pdf, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=null), CN=ArticleExt(id=1256260906397328132, articleId=1256260904723800820, tenantId=1146029695717560320, journalId=1256213323050663944, language=CN, title=大兴安岭南段多金属成矿带多源遥感蚀变信息提取研究——以内蒙古赤峰地区为例, columnId=1256260905998869249, journalTitle=地质与勘探, columnName=遥感地质专栏, runingTitle=null, highlight=null, articleAbstract=内蒙古赤峰北部地区拥有丰富的铅锌银锡钨铜锂铍铌钽钼铁矿产资源,是大兴安岭南段有色金属-稀有金属-银成矿带的重要组成区域。前人对大兴安岭南段,特别是赤峰地区开展了大量的地、物、化、遥和矿产等各方面的调查,但仍缺少有效的浅覆盖区找矿方法。考虑该区成矿地质背景、矿床的空间分布规律等综合因素,本文采用多源遥感技术对赤峰北部的克什克腾旗和林西县开展矿化蚀变信息提取。利用多光谱数据(Landsat-8、ASTER数据) 通过主成分分析法提取了不同类别的矿物蚀变信息;针对GF-5数据,采用混合调谐匹配滤波技术对矿化蚀变信息进行提取;再将三种遥感提取结果进行叠加分析,完成成矿靶区的圈定,并开展野外验证,进一步深化遥感在地质矿产资源调查领域的应用。基于Landsat-8、ASTER两种多光谱数据对铁染、羟基类(Mg-OH、Al-OH) 矿物信息进行了提取;基于GF-5数据识别出了白云母、绿帘石、高岭石3种蚀变矿物。经过野外验证发现,遥感提取结果与实际蚀变异常对应良好,并在圈定的靶区内发现了锡铜银铅锌矿化。本次研究结合不同数据源的提取与叠加结果,同时通过野外验证,印证了本文使用的矿化蚀变信息遥感提取方法的准确度。多光谱-高光谱遥感数据相结合有助于后续蚀变分带的分析与更精确的成矿预测,从而更好地服务于矿产资源调查等领域。, correspAuthors=代晶晶(1982年-),女,2013年毕业于中国地质大学(北京),地图制图学与地理信息工程专业,获博士学位,研究员,博士生导师,主要从事遥感地质方面研究工作。E-mail:daijingjing863@sina.com。, authorNote=吴畅宇(1999年-),男,2024年毕业于中国地质大学(北京),资源与环境遥感专业,获硕士学位,主要从事遥感地质方面研究工作。E-mail:wuchangyu199@163.com。, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=o3cHQByzGE5vTQv90VPImg==, pdfFileSize=7458974, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1 中国地质科学院矿产资源研究所,自然资源部成矿作用与资源评价重点实验室,北京 100037;
2 中国地质科学院矿产资源研究所,深地探测与矿产勘查全国重点实验室,北京 100094;
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大兴安岭南段多金属成矿带多源遥感蚀变信息提取研究——以内蒙古赤峰地区为例
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吴畅宇, 代晶晶, 武 广, 佘宏全, 庞绪勇, 赵天辰
地质与勘探 | 遥感地质专栏 2025,61(2): 230-242
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地质与勘探 | 遥感地质专栏 2025, 61(2): 230-242
大兴安岭南段多金属成矿带多源遥感蚀变信息提取研究——以内蒙古赤峰地区为例
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吴畅宇, 代晶晶, 武 广, 佘宏全, 庞绪勇, 赵天辰
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The extraction of multi-source remote sensing alteration information of polymetallic deposits in The southern Great Xing'an Range: A case study of the Chifeng area in Inner Mongolia
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doi: 10.12134/j.dzykt.2025.02.002
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内蒙古赤峰北部地区拥有丰富的铅锌银锡钨铜锂铍铌钽钼铁矿产资源,是大兴安岭南段有色金属-稀有金属-银成矿带的重要组成区域。前人对大兴安岭南段,特别是赤峰地区开展了大量的地、物、化、遥和矿产等各方面的调查,但仍缺少有效的浅覆盖区找矿方法。考虑该区成矿地质背景、矿床的空间分布规律等综合因素,本文采用多源遥感技术对赤峰北部的克什克腾旗和林西县开展矿化蚀变信息提取。利用多光谱数据(Landsat-8、ASTER数据) 通过主成分分析法提取了不同类别的矿物蚀变信息;针对GF-5数据,采用混合调谐匹配滤波技术对矿化蚀变信息进行提取;再将三种遥感提取结果进行叠加分析,完成成矿靶区的圈定,并开展野外验证,进一步深化遥感在地质矿产资源调查领域的应用。基于Landsat-8、ASTER两种多光谱数据对铁染、羟基类(Mg-OH、Al-OH) 矿物信息进行了提取;基于GF-5数据识别出了白云母、绿帘石、高岭石3种蚀变矿物。经过野外验证发现,遥感提取结果与实际蚀变异常对应良好,并在圈定的靶区内发现了锡铜银铅锌矿化。本次研究结合不同数据源的提取与叠加结果,同时通过野外验证,印证了本文使用的矿化蚀变信息遥感提取方法的准确度。多光谱-高光谱遥感数据相结合有助于后续蚀变分带的分析与更精确的成矿预测,从而更好地服务于矿产资源调查等领域。
多源遥感数据  /  蚀变信息提取  /  主成分分析  /  混合调谐匹配滤波  /  赤峰地区  /  内蒙古
The northern Chifeng region of Inner Mongolia contains abundant lead-zinc, silver, tin, tungsten, copper, lithium, beryllium, niobium,tantalum, molybdenum, and iron resources, making it significant in the nonferrous metal-rare metal-silver metallogenic belt of the southern GreatXing'an Range. Previous studies have conducted extensive geological, geophysical, geochemical, remote sensing, and mineral resource surveys in thesouthern Great Xing'an Range, particularly in the Chifeng area. However, there is still a lack of effective prospecting methods for shallow overburdenareas. Considering the metallogenic geological background and spatial distribution patterns of deposits in this region, this study employed multi-sourceremote sensing technology to extract mineralization alteration in Hexigten Banner and Linxi County, northern Chifeng. Multispectral data (Landsat-8,ASTER) were used to extract alteration of different mineral types through principal component analysis. For GF-5 data, the mixed-tuned matchedfiltering technique was applied to extract mineralization alteration. The results from the three remote sensing methods were then integrated forcomprehensive analysis to delineate prospective metallogenic targets. We also conducted field verification, further advancing the application of remote sensing in geological and mineral resource exploration. Based on Landsat-8 and ASTER multispectral data, iron-stained and hydroxyl-bearing (Mg-OH, Al-OH) mineral information was extracted. Three alteration minerals of muscovite, epidote, and kaolinite were identified based on the GF-5 data.Field verification confirmed that the remote sensing extraction results corresponded well with actual alteration anomalies, and tin-copper-silver-leadzincmineralization was discovered within the delineated target areas. This study, by combining extraction and overlay results from different data sourcesand validating them through field surveys, demonstrates the accuracy of the remote sensing methods used for mineralization alteration extraction. Theintegration of multispectral and hyperspectral remote sensing data facilitates subsequent alteration zoning analysis and more precise metallogenicprediction, thereby better serving mineral resource exploration and related fields.
multi-source remote sensing data, alteration information extraction, principal component analysis, hybrid tuned matched filtering,Chifeng area, Inner Mongolia
吴畅宇, 代晶晶, 武 广, 佘宏全, 庞绪勇, 赵天辰. 大兴安岭南段多金属成矿带多源遥感蚀变信息提取研究——以内蒙古赤峰地区为例. 地质与勘探, 2025 , 61 (2) : 230 -242 . DOI: 10.12134/j.dzykt.2025.02.002
. The extraction of multi-source remote sensing alteration information of polymetallic deposits in The southern Great Xing'an Range: A case study of the Chifeng area in Inner Mongolia[J]. Geology and Exploration, 2025 , 61 (2) : 230 -242 . DOI: 10.12134/j.dzykt.2025.02.002
2025年第61卷第2期
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doi: 10.12134/j.dzykt.2025.02.002
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2种不同金属材料的力学参数

Family
属数
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genus
种数
Number of
species
占总种数比例
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total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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