三维成矿预测关键问题

doi:10.13745/j.esf.sf.2024.5.9

摘要/Abstract

摘要：

三维成矿预测是当前深部找矿预测和勘查的重要方法和手段,其方法体系和实践应用均已取得大量成果,但同时存在若干关键科学技术问题,导致其进一步发展受到制约。本文从多尺度三维成矿预测方法体系不完善、不确定性分析与优化研究薄弱、三维成矿预测要素挖掘存在瓶颈、缺少针对三维成矿预测的三维深度学习模型和方法等关键问题出发,对目前三维成矿预测领域相关方面的研究进展进行综合分析,并提出针对上述关键问题可能的解决方案和研究方向。预期未来三维成矿预测领域的研究工作将创新发展出多种方法,实现对三维预测信息的深度挖掘;构建形成适用的三维深度学习模型和训练方法,有效增强三维成矿预测结果的预测能力;通过系统性地开展三维成矿预测不确定性研究,进一步优化预测过程和结果,有效提高三维成矿预测方法的可靠性和准确性;形成面向多尺度三维成矿预测的方法体系,更有效地指导矿集区-矿田-勘查区块(矿床)等不同级别的深部矿产资源找矿勘查工作。相关关键问题的解决将进一步深化和完善三维成矿预测理论和方法体系,促进三维成矿预测理论方法的实践应用,显著提升深部找矿预测和勘查工作的效率与水平,助力深部找矿突破。

关键词: 三维成矿预测, 关键问题, 多尺度, 预测信息发掘, 不确定性, 数据融合

Abstract:

Three-dimensional predictive modeling of mineral prospectivity is an important approach to deep mineral exploration. Although significant advancements have been made in the methodology and application of this approach, several key scientific and technological issues remain unresolved concerning the insufficiencies of multi-scale 3D predictive modeling methodology, uncertainty analysis and optimization of prediction results, mining of key factors in 3D mineralization prediction, and dedicated 3D deep learning models and methodologies tailored for 3D predictive modeling of mineral prospectivity. Focusing on these key issues, this paper conducts a comprehensive review of current research progress in the field, and proposes potential solutions and research directions to address these issues. Future developments in this field include methods for deep mining of 3D predictive information; applicable 3D deep learning models and training methods for enhanced predictive modeling; uncertainty analysis and optimization methods for improving the reliability and accuracy of 3D mineralization prediction; and a methodological framework for multi-scale predictive modeling so as to effectively guide deep mineral exploration at the levels of orebodies, ore fields, and ore deposits. Resolving these key issues will further develop and refine the theoretical and methodological frameworks of 3D mineralization prediction, significantly improve the efficiency of deep mineral exploration, and ultimately facilitate breakthrough in mineral deposit discovery in the deep earth.

Key words: 3D mineral prospectivity prediction, key issues, multi-scale, predictive information discovery, uncertainty, data fusion

中图分类号:

P624
P628

袁峰, 李晓晖, 田卫东, 周官群, 汪金菊, 葛粲, 国显正, 郑超杰. 三维成矿预测关键问题[J]. 地学前缘, 2024, 31(4): 119-128.

YUAN Feng, LI Xiaohui, TIAN Weidong, ZHOU Guanqun, WANG Jinju, GE Can, GUO Xianzheng, ZHENG Chaojie. Key issues in three-dimensional predictive modeling of mineral prospectivity[J]. Earth Science Frontiers, 2024, 31(4): 119-128.

图/表 5

图1 三维成矿预测关键问题

Fig.1 Key issues in 3D mineralization prediction

图2 三维成矿预测要素挖掘 (a引自文献[26];b引自文献[29];c引自文献[16];d引自文献[32-33]) a—三维物性反演;b—蚀变矿物微量元素指示斑岩系统中心;c—三维成矿过程数值模拟-体应变增量;d—化学反应速率与矿化强度。

Fig.2 Mining of key factors in 3D mineral prospectivity prediction. (a) Three-Dimensional physical property inversion (adapted from [26]). (b) Trace elements in altered minerals as an indicator for porphyry system center (adapted from [29]). (c) Numerical simulation of 3D mineralization process and volumetric strain increment (adapted from [16]). (d) Chemical reaction rate and intensity of mineralization (adapted from [32-33]).

图3 三维成矿预测中的三维卷积神经网络 a—多尺度三维卷积神经网络模型架构;b—三维样本集构建;c—小样本训练方法。

Fig.3 Deep learning for mineral prospectivity prediction. (a) Architecture of the proposed multi-scale 3D convolutional neural network. (b) Construction of the 3D sample set. (c) Method for training with a small sample size.

图4 三维成矿预测中的不确定分析 (a引自文献[76];b引自文献[78];c引自文献[77]) a—三维地质模型不确定性分析;b—预测数据缺失不确定性分析;c—三维成矿预测结果综合不确定性分析。

Fig.4 Uncertainty analysis on 3D geological model (a, adapted from [76]), prediction data loss (b, adapted from [78]), and 3D prediction result (c, adapted from [77]).

图5 多尺度三维地质模型(据文献[19,75,88⇓-90]修改)

Fig.5 Multi-scale 3D geological model. Modified after [19,75,88⇓-90].

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