利用ASTER卫星图像识别摩洛哥东部反阿特拉斯热液蚀变岩——以Imiter银矿为例

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2021-07-27 DOI:10.1080/19479832.2021.1958928

Y. Atif, A. Soulaimani, Atman Ait lamqadem, A. B. Pour, B. Pradhan, El Aouad Nouamane, Kharis Abdelali, A. M. Muslim, M. S. Hossain

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引用次数: 8

摘要

Imiter银矿位于摩洛哥东部的东反阿特拉斯成矿省，是一个世界级的银矿床。该地区具有未发现银矿化的潜力，值得进行详细的遥感研究。本研究将Crosta、频带比和混合调谐匹配滤波(MTMF)方法应用于ASTER遥感数据。利用专门的波段比和Crosta技术确定了泥质、叶质和丙质蚀变带。采用MTMF算法检测针铁矿、赤铁矿、褐铁矿、白云母/伊利石、绿泥石/绿帘石、黄铁矾和高岭石/明矾石的亚像素丰度。据此，在研究区中部、北部、南部和东北部划分了几个蚀变带，可视为高远景带。GPS测量、薄片和抛光切片分析、XRD和地球化学测量验证了高远景带的蚀变带和硫化物矿化。该方法可应用于东反阿特拉斯成矿省其他地区的热液矿床找矿。

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Identifying hydrothermally altered rocks using ASTER satellite imageries in Eastern Anti-Atlas of Morocco: a case study from Imiter silver mine

ABSTRACT The Imiter silver mine in the Eastern Anti-Atlas metallogenic province, Eastern Morocco, is a world-class silver ore deposit. This region has potential for undiscovered silver mineralisation and deserves a detailed remote-sensing study. In this study, Crosta, band ratios and mixture-tuned matched-filtering (MTMF) methods were applied to ASTER remote-sensing data. Argillic, phyllic and propylitic alteration zones were identified using specialised band ratios and Crosta techniques. Sub-pixel abundances of goethite, haematite, limonite, muscovite/illite, chlorite/epidote, jarosite and kaolinite/alunite were detected using MTMF algorithm. Accordingly, several alteration zones were identified and delimited in the central, northern, southern and northeastern parts of the study region, which can be considered as high prospective zones. GPS survey, analysis of thin and polish sections, XRD and geochemical survey verified the alteration zones and sulphide mineralisation in high prospective zones. This approach can be applied in other parts of the Eastern Anti-Atlas metallogenic province to explore hydrothermal ore deposits.

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).