大数据时代矿床学研究发展状况综述

A review on the development of mineral deposit science in the era of big data

近年来矿床学的研究出现了如成矿模式创新性不足以及成矿系列与成矿规律视角单一等瓶颈。回顾矿床学的发展历程表明,矿床学研究的每一次突破和飞跃,都与新科学技术的发展息息相关。随着“大数据”“智能化”时代的来临,人工智能大数据深度学习等新技术正在蓬勃发展。地质数据具有“大数据”的大量性、高速性、多样性和价值性的“4V”的特征,还有多元性、多维性、多源性、异构性、时空性等特点。通过对近十余年国内外相关文献的统计对比分析,对人工智能、机器学习、深度学习等之间的从属关系和主要特征进行了阐述,对随机森林算法、卷积神经网络、决策树算法、朴素贝叶斯和支持向量机等算法在矿床研究中的实例进行了梳理,认为人工智能技术引领全球矿产资源智能勘查研究将成为矿床学研究发展的必然方向。

In recent years,there have been some bottlenecks in the study of mineral deposits,such as.the innovation deficienciences of metallogenic model and the monotorous perspective of metallogenic series and metallogeny.This paper reviewed the development history of ore deposit science and pointed out that every breakthrough and leap in ore deposit science is closely related to the development of new science and technology.With the continuous improvement of scientific progress,especially the coming of the‘big data’and‘intelligent’era,new technologies such as deep learning of big data in artificial intelligence are developing vigorously.Geological big data has the characteristics of‘4V’of‘big data’,such as‘volume’,‘velocity’,‘variety’and‘value’,as well as the characteristics of pluralism,multi-dimension,multi-source,heterogeneity and space-time.Based on the statistical comparison and analysis of relevant domestic and foreign literatures in the past decade,this paper expounded the subordinate relationship and main characteristics among artificial intelligence,machine learning and deep learning,and the examples of random forest algorithm,convolutional neural network,decision tree algorithm,Naive Bayes algorithm and support vector machine and other algorithms in ore deposit research were also sorted out.In this paper,it was considered that the study of intelligent exploration of global mineral resources that lead by the artificial intelligence technology will become the inevitable direction of the future development of ore deposit science.