利用机器学习算法从地球物理数据推断三维断层结构和覆盖层深度--加拿大魁北克 Fenelon 金矿床案例研究

IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Geophysical Prospecting Pub Date : 2024-08-16 DOI:10.1111/1365-2478.13589
Limin Xu, E. C. R. Green, C. Kelly
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引用次数: 0

摘要

我们采用机器学习方法,在加拿大魁北克省 Archean Fenelon 金矿床内部和周围 18 平方公里的研究区域内,自动推断出两个关键属性--断层或剪切带结构的位置以及覆盖层的厚度。我们的方法包括反演在地表以下 480 米处截断的经过精心策划的钻孔岩性和构造观测数据,并结合磁力和光探测与测距勘测数据。我们采用的是一种计算成本较低的方法,不强求地质一致性的基础模型。我们研究了三种截然不同的方法:(1) 推断断层模型,即利用钻孔观测直接评估断层或剪切带的存在;(2) 推断覆盖层模型,即利用对覆盖层-基岩接触面的钻孔观测;(3) 包含三个类别--覆盖层、断层基岩和非断层基岩--的模型,该模型综合了(1)和(2)的各个方面。在每种情况下,我们都采用了全部 32 种标准机器学习算法。我们发现,袋状树算法、精细 K 近邻算法和加权 K 近邻算法最为成功,其准确性、灵敏度和特异性指标相似。袋状树算法预测故障位置的准确率约为 80%,灵敏度为 70%,特异性为 73%。对覆盖层厚度的预测准确率为 99%,灵敏度为 77%,特异性为 93%。从质量上看,断层位置预测与独立构建的地质解释结果相比效果良好。类似的方法可能适用于钻孔覆盖率较高的其他地区,前提是在为机器学习训练集设计分类时严格遵守钻孔测井中使用的标准,并可使用各种地球物理勘测数据类型作为有益的补充。
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Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada

We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km2 study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low-cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden-bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K-nearest neighbours and weighted K-nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types.

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来源期刊
Geophysical Prospecting
Geophysical Prospecting 地学-地球化学与地球物理
CiteScore
4.90
自引率
11.50%
发文量
118
审稿时长
4.5 months
期刊介绍: Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.
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Issue Information Simultaneous inversion of four physical parameters of hydrate reservoir for high accuracy porosity estimation A mollifier approach to seismic data representation Analytic solutions for effective elastic moduli of isotropic solids containing oblate spheroid pores with critical porosity An efficient pseudoelastic pure P-mode wave equation and the implementation of the free surface boundary condition
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