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
  • [31] A New Evaluation Metrics for Block-based Python']Python Code
    Liu, Zheng
    Luo, Hong
    Chai, Xiaolin
    2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2019,
  • [32] Solar Energy Block-Based Residential Construction for Rural Areas in the West of China
    Shao, Jizhong
    Chen, Huixian
    Zhu, Ting
    SUSTAINABILITY, 2016, 8 (04)
  • [33] Design and Evaluation of a Block-based Environment with a Data Science Context
    Bart, Austin Cory
    Tibau, Javier
    Kafura, Dennis
    Shaffer, Clifford A.
    Tilevich, Eli
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2020, 8 (01) : 182 - 192
  • [34] Block-Based Programming for Mobile with Conventional Exceptions and Automatic Evaluation
    Atashpendar, Aryobarzan
    Rothkugel, Steffen
    PROCEEDINGS OF THE 2024 CONFERENCE INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, VOL 1, ITICSE 2024, 2024, : 597 - 603
  • [35] Understanding students' abstractions in block-based programming environments: A performance based evaluation
    Cakiroglu, Unal
    Cevik, Isak
    Koseli, Engin
    Aydin, Merve
    THINKING SKILLS AND CREATIVITY, 2021, 41
  • [36] Block-based motion estimation using the pixelwise classification of the motion compensation error
    Kim, JY
    Park, RH
    Yang, SJ
    ICCE: 2005 INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, DIGEST OF TECHNICAL PAPERS, 2005, : 259 - 260
  • [37] Block-based compressive sensing in deep learning using AlexNet for vegetable classification
    Irawati, Indrarini Dyah
    Budiman, Gelar
    Saidah, Sofia
    Rahmadiani, Suci
    Latip, Rohaya
    PEERJ COMPUTER SCIENCE, 2023, 9
  • [38] Real-Time Block-Based Embedded CNN for Gesture Classification on an FPGA
    Wang, Ching-Chen
    Ding, Yu-Chun
    Chiu, Ching-Te
    Huang, Chao-Tsung
    Cheng, Yen-Yu
    Sun, Shih-Yi
    Cheng, Chih-Han
    Kuo, Hsueh-Kai
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2021, 68 (10) : 4182 - 4193
  • [39] Block-based semantic classification of high-resolution multispectral aerial images
    Avramovic, Aleksej
    Risojevic, Vladimir
    SIGNAL IMAGE AND VIDEO PROCESSING, 2016, 10 (01) : 75 - 84
  • [40] Block-based cloud classification with statistical features and distribution of local texture features
    Cheng, H. -Y.
    Yu, C. -C.
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2015, 8 (03) : 1173 - 1182