基于卷积神经网络和黄铁矿大数据判别金矿类型
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本文为中国地质调查局地质调查工作项目(编号12120114014201)、中央高校基本科研业务费专项资金项目(编号310827173702,300102280401)、陕西省自然科学基金项目(编号2023- JC- YB- 261)和陕西省地勘基金项目(编号214027160195)联合资助的成果


Discrimination of gold deposit types based on convolutional neural network and pyrite big data
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    摘要:

    黄铁矿是金矿中普遍存在的金属硫化物,其微量元素含量信息可以揭示出矿物形成时的相关矿物和流体组成与结晶特征,因此不同期次与金矿有关的黄铁矿及微量元素信息可以被用来预测矿床的类型。且相关研究历史已久,积累了大量的研究资料,然而由于研究思想和手段的局限性,传统方法往往存在无解性或多解性问题。随着大数据思想的发展和推广,研究人员试图采用机器学习算法来解决此类问题,已取得不错的效果。本次研究根据深度学习思想建立黄铁矿微量元素数据集并进行深入研究,采用“成分数据图像化”和“数据增强”等手段,解决了前人采用深度学习方法进行此类分类任务时遇到的数据不平衡问题和卷积神经网络无法直接读取数据的问题。本文对比分析了基于四种卷积神经网络模型(Mobilenet V2、Resnet 50、VGG 16和VIT)采用黄铁矿微量元素成分数据进行金矿类型分类任务的精度与效果,发现采用卷积神经网络算法可以基于黄铁矿成分数据对不同类型金矿进行较为精准的分类任务。此方法比传统图解法具备更高的精准度与泛化能力,通过对金矿类型的预测可以为找矿勘查和深部预测工作节省成本,也为深度学习在地质矿产研究方面的应用和推广提供全新思路,具有一定的推广价值。

    Abstract:

    Pyrite is a ubiquitous metal sulfide in gold mines, and its trace element content information can reveal the composition and crystallization characteristics of related minerals and fluids when the minerals are formed. Therefore, different stages of pyrite trace element information can be used to predict the type of deposit. In addition, the related research has a long history, and a large amount of data have been accumulated. However, due to the limitations of research ideas and methods, traditional methods often have problems of no solution or multiple solutions. With the development and promotion of big data ideas, researchers have tried to use machine learning algorithms to solve such problems, and have achieved good results. In this study, based on the idea of deep learning, a data set of trace elements of pyrite was established and subjected to in- depth research. By means of “component data visualization” and “data enhancement”, it solved the problems encountered by predecessors when using deep learning methods for such classification tasks. Previous attempts have pointed to the data imbalance problem and the inability of the convolutional neural network to directly read the data. This paper compares and analyzes the accuracy and effect of gold ore type classification task using pyrite trace element composition data based on four convolutional neural network models (Mobilenet V2, Resnet 50, VGG 16 and VIT). It is found that the convolutional neural network algorithm based on the pyrite composition data can perform more accurate classification tasks for different types of gold deposits. This method has higher accuracy and generalization ability than the traditional graphical method. By predicting the type of gold deposits, it can save costs for prospecting exploration and deep prediction work, and also provide deep learning for the application and promotion of geological and mineral resources research. The new idea has certain promotion value.

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引用本文

朱昊磊,杨兴科,何虎军,李展,张鑫雨,崔文玮.2023.基于卷积神经网络和黄铁矿大数据判别金矿类型[J].地质学报,97(10):3396-3409.
ZHU Haolei, YANG Xingke, HE Hujun, LI Zhan, ZHANG Xinyu, CUI Wenwei.2023. Discrimination of gold deposit types based on convolutional neural network and pyrite big data[J]. Acta Geologica Sinica,97(10):3396-3409.

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  • 收稿日期:2022-04-12
  • 最后修改日期:2022-05-23
  • 录用日期:2022-05-29
  • 在线发布日期: 2023-10-28
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