贝叶斯深度学习集成地球物理与钻探数据进行铜矿化三维预测与钻探靶区定位：以鲁德内阿尔泰科戈代伊远景区为例
Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai
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中文总结 AI 辅助
提出贝叶斯深度学习三维工作流，联合钻探与地球物理数据预测铜品位场并量化不确定性，应用于科戈代伊远景区，辅助风险感知钻探靶区定位。
中文摘要 AI 辅助
在构造复杂的地体中，勘探钻探靶区定位受到稀疏采样、异构数据集以及地球物理反演模糊性的阻碍。在此，我们提出了一种不确定性感知的三维工作流程，以加速棕地勘探中的发现时间，并将其应用于鲁德内阿尔泰成矿省的科戈代伊远景区。我们采用综合方法联合分析现有的钻探和地球物理数据，揭示已有数据中隐藏的模式。钻孔和探槽被重新测量至统一的三维参考框架，化验结果被组合至一致的空间支撑，以促进与地球物理输入的联合建模。我们开发了贝叶斯深度学习模型，用于预测铜品位的三维场以及充电率和视电阻率，同时通过蒙特卡洛采样量化认知不确定性。本工作的原创贡献在于，不将问题视为同位观测之间的逐点回归，而是从稀疏、异构的勘探证据中联合学习空间连续的三维场。所得三维预测描绘了主要矿化趋势和几个局部候选区域，这些区域与增强的激发极化（IP）响应相吻合，而不确定性映射则突出了预测稳健的区域与额外钻探最具信息量的区域。连续的铜品位场还可通过阈值化生成二元远景图，从而评估靶区圈定对所选截止品位的敏感性。输出旨在用于定性解释和风险感知的钻探靶区定位，而非资源估算，我们讨论了因地球物理产品来源元数据不完整和历史采样异质性而产生的关键局限性。
英文摘要
Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the Kogodai prospect in the Rudny Altai metallogenic province. We jointly analyse existing drilling and geophysical data in a comprehensive approach, revealing hidden patterns in already available data. Drillholes and trenches were desurveyed to a common 3D reference frame, and assays were composited to a consistent spatial support to facilitate joint modelling with geophysical inputs. We develop Bayesian deep-learning models to predict 3D fields of Cu grade together with chargeability and apparent resistivity while quantifying epistemic uncertainty via Monte Carlo sampling. The original contribution of this work is to treat the problem not as pointwise regression between co-located observations, but as joint learning of spatially continuous 3D fields from sparse, heterogeneous exploration evidence. The resulting 3D predictions delineate a principal mineralized trend and several localized candidate zones that coincide with elevated induced polarization (IP) responses, while uncertainty mapping highlights where predictions are robust versus where additional drilling would be most informative. The continuous Cu-grade field can also be thresholded to produce binary prospectivity maps, allowing the sensitivity of target delineation to the chosen cutoff to be evaluated. The outputs are intended for qualitative interpretation and risk-aware drill targeting rather than resource estimation, and we discuss key limitations arising from incomplete provenance metadata for geophysical products and heterogeneity of historical sampling.
发表机构
- Terra Quantum AG（泰拉量子公司）
- Kogodai Joint Venture LLP（科戈代合资有限责任合伙公司）
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