Collaborative Machine Learning: using information from multiple mineral deposits to improve decision making

协作机器学习：利用多个矿藏的信息来改进决策

• 批准号: 577571-2022
• 负责人: Boisvert, JeffJ
• 金额: $ 14.02万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748887
• 关键词: Collaborative; Machine; Learning; using; information

Drillhole samples and remote sensing surveys provide information on subsurface resources for mineral resource estimation, mine planning, and project evaluation. The collection of this data is expensive but necessary to evaluate mineral resources and build mine plans. Additional data collected reduces subsurface uncertainty but is expensive to collect. Machine learning algorithms are becoming common in subsurface modeling workflows and as tools to assist decision making, but these algorithms perform better with access to more training data. This project explores methodologies for applying machine learning methods to subsurface modeling problems using training data from multiple datasets held by different companies. Mining companies rarely publicize data because of the data collection expense and desire for competitive advantage. Some algorithm training environments, such as federated learning, allow for multiple datasets to be remotely compiled to learn underlying relations between variables in a collaborative way; in this framework, individual datasets remain private to each company that owns the databases but allows for learnings between datasets/companies. This can be combined with transfer learning, which focuses on using knowledge gained from a previous problem (or dataset) applied to a new problem (or dataset). These methods can incorporate knowledge from geological settings with dense data (i.e. mined out deposits) to settings with sparse data (i.e. exploration) to improve subsurface modeling and decision making. This allows for the development of improved machine learning algorithms while maintaining data privacy and competitive advantages. Subsurface modeling problems addressed in this work include core logging analysis (unsupervised clustering), rock type assignment (supervised clustering), geometallurgical modeling (artificial neural networks) and spatial uncertainty modeling (convolutional neural networks).

钻孔取样和遥感勘测为矿产资源估算、矿山规划和项目评估提供地下资源信息。收集这些数据是昂贵的，但对评估矿产资源和制定采矿计划是必要的。机器学习算法在地下建模工作流中越来越常见，并作为辅助决策的工具，但这些算法在访问更多训练数据时表现更好。该项目探索了将机器学习方法应用于地下建模问题的方法，使用来自不同公司持有的多个数据集的训练数据。矿业公司很少公布数据，因为数据收集费用和竞争优势的愿望。一些算法训练环境，如联合学习，允许远程编译多个数据集，以协作方式学习变量之间的潜在关系;在这个框架中，单个数据集对拥有数据库的每个公司保持私有，但允许数据集/公司之间的学习。这可以与迁移学习相结合，迁移学习侧重于使用从先前问题（或数据集）中获得的知识应用于新问题（或数据集）。这些方法可以将来自具有密集数据（即开采出的矿床）的地质环境的知识结合到具有稀疏数据（即勘探）的环境，以改进地下建模和决策制定。这允许开发改进的机器学习算法，同时保持数据隐私和竞争优势。在这项工作中解决的地下建模问题包括岩心测井分析（无监督聚类），岩石类型分配（监督聚类），几何建模（人工神经网络）和空间不确定性建模（卷积神经网络）。