现代地质2025，Vol.39Issue(3)：541-551,11.DOI:10.19657/j.geoscience.1000-8527.2024.077
基于深度学习的安徽黄屯铜金矿床黄铜矿定量分析及其意义
Quantitative Analysis of Chalcopyrite in Huangtun Copper-Gold Deposit in Anhui Province Based on Deep Learning and Its Significance
摘要
Abstract
Over the years,quantitative indicators in the upstream(exploration)to downstream(mining,bene-ficiation,and smelting)phases of the industrial chain have primarily relied on valuable elements.However,due to the diversity of minerals hosting these elements(where a single element can be present in multiple miner-al phases),relying solely on elemental quantification often leads to significant discrepancies between resource reserve assessments in the early stages and resource recovery evaluations later on.Evidently,as the beneficia-tion process aims to recover target minerals containing recoverable elements,it is more reasonable to use the content of target minerals,rather than target elements,as the quantitative indicator.Consequently,for recover-able target minerals,the application of artificial intelligence image recognition technology for rapid and accurate quantification merits further investigation.The Huangtun copper-gold deposit in Anhui,a hydrothermal deposit,has copper(Cu)as one of its primary recoverable resources,with chalcopyrite being the primary mineral phase hosting Cu.Through the use of deep learning methods for microscopic image analysis,chalcopyrite was identi-fied and quantified in 114 samples from 13 boreholes across five exploration lines in the mining area.The results demonstrate that AI image recognition technology based on deep learning can accurately identify and quantify chalcopyrite,providing a more comprehensive understanding of the spatial variation and distribution of copper resources compared to elemental quantification.This has significant implications for guiding exploration,min-ing,beneficiation,and separation of deep copper deposits or related minerals(such as gold),ultimately enhan-cing the comprehensive utilization efficiency of resources.关键词
黄铜矿/深度学习/矿物定量/黄屯铜金矿床Key words
chalcopyrite/deep learning/mineral quantification/Huangtun copper-gold deposit分类
地质学引用本文复制引用
唐赧钰,张萌萌,林浩,王浩阳,申俊峰,陈强,卿敏,赵永建,刘海明,董博,陈满,龚智诚..基于深度学习的安徽黄屯铜金矿床黄铜矿定量分析及其意义[J].现代地质,2025,39(3):541-551,11.基金项目
教育部科技发展中心中国高校产学研创新基金项目(KBB036). （KBB036）
