Integrate physics-driven dynamics simulation with data-driven machine learning to predict potential targets in maturely explored orefields: A case study in Tongguangshan orefield, Tongling, China

IF 3.4 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Liangming Liu , Feifu Zhou , Wei Cao
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Abstract

The physics-driven dynamics simulation (DS) and data-driven machine learning (ML) are two general approaches to predict complex systems whose complexity is a hardship impediment to prediction. Based on the 3D geological modeling (GD), we embedded the DS into ML to predict high potential targets and to evaluate ore-controlling and ore-indicating factors in the Tongguangshan (TGS) skarn orefield that has undergone intensive exploration and 4 Cu and Au deposits discovered. The 3D geological models show that the heterogeneous distribution of orebodies around intrusions is associated with the wall rock lithology and contact zone (CZ) characteristics of intrusions, and the resistivity can only provide some ambiguous clues for interpretation of underground geological architectures rather than a direct ore-indicator. The DS results show heterogeneous distribution of temperature, pore pressure, differential stress, volume strain and shear strain, among which the volume strain is closest associated with ore formation. Based on the prediction of Random Forest (FR) model of which the feature variables are combination of DS and 3D modeling results, the SHAP valuing results show a descending importance rank of ore-controlling factors and ore-indicators as lithology, volume strain, distance to CZ, distance to Devonian-Carboniferous interface, curvature of CZ, pressure, temperature, CZ azimuth, resistivity, differential stress, shear strain and CZ dip. The DS results are more important than the resistivity. We have run 6 RF models, consisting of different feature variables which were assigned by DS and 3D modeling, to predict ore-formation favor spaces. The prediction performances on test data sets suggest that, integrating of geological features with dynamics features can enhance performance of RF prediction, the RF model consisting of pure dynamics features can predict mineralization different from the training samples. All RF models' predictions support that there are no significant high potentials at the depth of the orefield, except one small target at its eastern south corner.

Abstract Image

将物理驱动的动力学模拟与数据驱动的机器学习相结合,预测已勘探成熟矿田的潜在目标:中国铜陵铜官山矿田案例研究
物理驱动的动力学模拟(DS)和数据驱动的机器学习(ML)是预测复杂系统的两种通用方法。在三维地质模型(GD)的基础上,我们将动力学模拟嵌入到机器学习中,对经过深入勘探并发现了 4 个铜金矿床的铜官山矽卡岩矿田的高潜力目标进行预测,并对控矿和诱矿因素进行评估。三维地质模型显示,侵入体周围矿体的异质分布与侵入体的壁岩岩性和接触带(CZ)特征有关,电阻率只能为地下地质构造的解释提供一些模糊的线索,而不是直接的矿石指示剂。电阻率结果显示温度、孔隙压力、应力差、体积应变和剪切应变的异质性分布,其中体积应变与成矿关系最为密切。随机森林(FR)模型的特征变量是 DS 和三维建模结果的组合,根据随机森林(FR)模型的预测,SHAP 估值结果显示,控矿因素和成矿指标的重要程度由高到低依次为岩性、体积应变、到 CZ 的距离、到泥盆系-石炭系界面的距离、CZ 的曲率、压力、温度、CZ 方位角、电阻率、应力差、剪切应变和 CZ 倾角。差应力的结果比电阻率更重要。我们运行了 6 个 RF 模型,这些模型由 DS 和三维建模分配的不同特征变量组成,用于预测成矿有利空间。测试数据集的预测结果表明,将地质特征与动力学特征相结合可以提高射频预测的性能,而由纯动力学特征组成的射频模型则可以预测与训练样本不同的矿化度。所有射频模型的预测结果都表明,除了矿田东部南角的一个小目标外,矿田深处没有明显的高电位。
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来源期刊
Journal of Geochemical Exploration
Journal of Geochemical Exploration 地学-地球化学与地球物理
CiteScore
7.40
自引率
7.70%
发文量
148
审稿时长
8.1 months
期刊介绍: Journal of Geochemical Exploration is mostly dedicated to publication of original studies in exploration and environmental geochemistry and related topics. Contributions considered of prevalent interest for the journal include researches based on the application of innovative methods to: define the genesis and the evolution of mineral deposits including transfer of elements in large-scale mineralized areas. analyze complex systems at the boundaries between bio-geochemistry, metal transport and mineral accumulation. evaluate effects of historical mining activities on the surface environment. trace pollutant sources and define their fate and transport models in the near-surface and surface environments involving solid, fluid and aerial matrices. assess and quantify natural and technogenic radioactivity in the environment. determine geochemical anomalies and set baseline reference values using compositional data analysis, multivariate statistics and geo-spatial analysis. assess the impacts of anthropogenic contamination on ecosystems and human health at local and regional scale to prioritize and classify risks through deterministic and stochastic approaches. Papers dedicated to the presentation of newly developed methods in analytical geochemistry to be applied in the field or in laboratory are also within the topics of interest for the journal.
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