Evaluation and Classification of Uranium Prospective Areas in Madagascar: A Geochemical Block-Based Approach

被引:0
|
作者
Wu, Datian [1 ,2 ,3 ]
Liu, Jun'an [2 ]
Razoeliarimalala, Mirana [4 ]
Wang, Tiangang [2 ]
Razafimbelo, Rachel [4 ]
Xu, Fengming [3 ]
Sun, Wei [3 ]
Ralison, Bruno [4 ]
Wang, Zhuo [3 ]
Zhou, Yongheng [3 ]
Zhao, Yuandong [3 ,5 ]
Zhao, Jun [3 ,6 ]
机构
[1] China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China
[2] China Geol Survey, Nanjing Ctr, Nanjing 210016, Peoples R China
[3] China Geol Survey, Shenyang Ctr, Shenyang 110034, Peoples R China
[4] Univ Antananarivo, Fac Sci, Ment Sci Terre & Environm, Antananarivo 101, Madagascar
[5] China Geol Survey, Mudanjiang Ctr, Mudanjiang 157000, Peoples R China
[6] China Geol Survey, Xian Ctr, Xian 710000, Peoples R China
关键词
geochemical block; uranium ore prospective area; 1/1 million low-density geochemistry; Madagascar; ELEMENT GRANITIC PEGMATITES; MINERALIZATION; MARBLES;
D O I
10.3390/min15030280
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The Precambrian crystalline basement of Madagascar, shaped by its diverse geological history of magmatic activity, sedimentation, and metamorphism, is divided into six distinct geological units. Within this intricate geological framework, five primary types of uranium deposits are present. Despite the presence of these deposits, their resource potential remains largely unquantified. To address this, a comprehensive study was conducted on Madagascar's uranium geochemical blocks. This study processed the original data of uranium elements across the region, following the "Theoretical Model Pedigree of Geochemical Block Mineralization" proposed by Xie Xuejin. The analysis is based on the geochemical mapping data of Madagascar at a scale of 1:100,000, which was jointly completed by the China-Madagascar team and involved the delineation of geochemical blocks and the division of their internal structures using the 15 km x 15 km window data. The study used an isoline with a uranium content greater than 3.2 x 10-6 as a boundary and considered five key factors for the classification of prospective areas. These factors included uranium bulk density, anomaly intensity, block structure, prospective area, and the tracing of uranium enrichment trajectories through the pedigree chart of 5-level geochemical blocks. By integrating these factors with potential resource assessment, uranium mining economics, and conditions for uranium mining and utilization, the study successfully classified and evaluated uranium resources in Madagascar. As a result, 10 uranium prospective areas were identified, ranging from Level I to IV, with 3 being Level I areas deemed highly promising for exploration and investment. For the first time, the study predicted a resource potential of 72,600 t of uranium resources, marking a significant step towards understanding Madagascar's uranium endowment.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Block-Based Noisy/Clean Classification of Images Using the Common Vector Approach
    Kalyoncu, Hasan Basar
    Ergin, Semih
    Gulmezoglu, Mehmet Bilginer
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2020, 39 (03) : 1387 - 1418
  • [2] Block-Based Noisy/Clean Classification of Images Using the Common Vector Approach
    Hasan Basar Kalyoncu
    Semih Ergin
    Mehmet Bilginer Gulmezoglu
    Circuits, Systems, and Signal Processing, 2020, 39 : 1387 - 1418
  • [3] A knowledge-based, transferable approach for block-based urban land-use classification
    Novack, Tessio
    Kux, Hermann
    Feitosa, Raul Q.
    Costa, Gilson A. O. P.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (13) : 4739 - 4757
  • [4] Block-based approach to solving linear systems
    Tiyyagura, Sunil R.
    Kuster, Uwe
    COMPUTATIONAL SCIENCE - ICCS 2007, PT 1, PROCEEDINGS, 2007, 4487 : 128 - +
  • [5] A New Approach to the Block-based Compressive Sensing
    Tian, Sen
    Ye, Songtao
    Iqbal, Muhammad Faisal Buland
    Zhang, Jin
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS AND DIGITAL IMAGE PROCESSING (CGDIP 2017), 2017,
  • [6] ADAPTIVE BLOCK-BASED APPROACH TO IMAGE STABILIZATION
    Tico, Marius
    2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5, 2008, : 521 - 524
  • [7] A Block-Based Regularized Approach for Image Interpolation
    Chen, Li
    Huang, Xiaotong
    Tian, Jing
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [8] Pixelizing data cubes:A block-based approach
    Choong, Yeow Wei
    Laurent, Anne
    Laurent, Dominique
    PIXELIZATION PARADIGM, 2007, 4370 : 63 - +
  • [9] Performance Evaluation of Block-Based Adaptive Algorithms
    Nikolic, T.
    Talaska, T.
    Nikolic, G.
    Dlugosz, R.
    2019 IEEE 31ST INTERNATIONAL CONFERENCE ON MICROELECTRONICS (MIEL 2019), 2019, : 285 - 288
  • [10] A fast block-based approach for segmentation and classification of textural images using contourlet transform and SVM
    Taghanaki, Soroosh Rahimi
    Javidan, Reza
    INTERNATIONAL JOURNAL OF SIGNAL AND IMAGING SYSTEMS ENGINEERING, 2014, 7 (04) : 211 - 219