Hegab, M.A. (2021) Remote Sensing and Gamma-Ray Spectrometry Based Gold Related Alteration Zones Detection: Case Study (Um Balad Area), North Eastern Desert, Egypt. Pure and Applied Geophysics, 178, 3909-3931.
https://doi.org/10.1007/s00024-021-02865-1

has been cited by the following article:

• TITLE: Research on Prospecting Prediction Method Based on Multi-Source Remote Sensing Images—A Case Study of Northern Namibia

  AUTHORS: Zhen Chen

  KEYWORDS: Convolutional Neural Network (CNN), Remote Sensing Data, Mineral Mapping, Copper Mines in Namibia, Prospecting

  JOURNAL NAME: Advances in Remote Sensing, Vol.14 No.1, March 6, 2025

  ABSTRACT: The mining area in northern Namibia is rich in mineral resources, but the geological structure is complex, and the traditional mineral exploration technology is faced with certain challenges. In this paper, a method of convolutional neural network (CNN) combined with remote sensing data is proposed to delineate the prospecting potential area in this area. By means of calcite mineral distribution map, chlorite mineral distribution map, lithologic structure interpretation map, iron dye hydroxyl alteration map, according to the known ore points, and finally using CNN to classify, the prospecting prospect map is generated, revealing the spatial distribution characteristics of potential mineralization zones. The research results show that CNN technology can effectively improve the accuracy of mineral resources assessment, and help to identify hidden ore bodies, showing a wide application potential in the future mineral exploration.