摘要
深部综合信息矿产资源预测评价研究是地球系统科学研究中最具有交叉学科性质的领域。随着地表矿、浅部矿、易识别矿的日益减少,地质找矿工作逐步向第二深度空间发展。深部矿产资源三维预测已成为当前成矿预测研究的重点领域,地质学家们经过长期的持续研究,在该领域取得了一系列重大成果与重要认识。《地球学报》集中在2020年第2期刊发15篇文章作为“深部综合信息矿产资源预测评价”专辑,专辑涵盖了三个方向的研究成果:(1)矿产资源三维预测;(2)地质调查与研究;(3)地质、地化数据处理方法等。这些工作主要涉及矿产资源预测评价的两个方面,即预测方法和成矿规律,具体包括深部构造三维重建技术方法、同位素定年及示踪在成矿规律研究中的应用以及地球化学异常信息机器学习提取方法等方面的内容。本文将简要介绍收入本专辑论文的研究工作,对深入研究和认识深部综合信息矿产资源预测评价提供一定参考价值。
Prediction and assessment of deep mineral resources based on integrated geoinformation is the most interdisciplinary field in earth system science.With the decrease of surface deposits,shallow deposits and deposits which are easily identified,geological prospecting has gradually developed toward the second deep space.The three-dimensional prediction has become the key field of current metallogenic prediction research.Based on a long-term continuous research,geologists have obtained a series of major achievements and important understandings in the three-dimensional prediction.The special issue(No.2,2020)of Acta Geoscientica Sinica contains 15 papers concerning the prediction and assessment of deep mineral resources based on integrated geoinformation.This special issue covers the following three subjects:(1)three dimensional prediction,(2)geological survey and research,and(3)geological and geochemical data processing methods.These papers mainly discuss the three-dimensional reconstruction method of deep structure,the application of isotopic dating and tracing in metallogenic regularity study,and the geochemical anomaly analysis method.This introduction aims to briefly describe each study in the three subjects,in order to provide some reference for the further studying and understanding of the prediction and assessment of deep mineral resources.
作者
肖克炎
XIAO Ke-yan(MNR Key Laboratory of Metallogeny and Mineral Assessment,Institute of Mineral Resources,Chinese Academy of Geological Sciences,Beijing 100037)
出处
《地球学报》
EI
CAS
CSCD
北大核心
2020年第2期130-134,共5页
Acta Geoscientica Sinica
关键词
三维预测
成矿构造三维模型
成矿规律
机器学习
three dimensional prediction
three dimensional model of metallogenic structure
metallogenic regularity
machine learning
