Single-sample unmixing with machine learning: a rock magnetic frontier

通过机器学习进行单样本分离：岩石磁性前沿

• 批准号: DP200100765
• 负责人: Prof Andrew Roberts
• 金额: $ 29.78万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200100765
• 关键词: Single; sample; unmixing; machine; learning

Magnetic rock-forming minerals can record important information about Earth’s magnetic field and climatic changes. In rock magnetism, we seek to quantify magnetic property variations in geological materials. Existing quantification methods are limited and provide bulk characterisation of all magnetic particles in a material rather than diagnostic information concerning individual mineral components. This Project aims to develop a machine-learning framework to “unmix” and quantify each magnetic mineral component in single natural samples, and will unlock a new quantitative era in rock magnetism. It is expected to have impact beyond Earth science by enabling magnetic characterisation in physics, materials science, and industry.

磁性造岩矿物可以记录有关地球磁场和气候变化的重要信息。在岩石磁学中，我们试图量化地质材料中的磁性变化。现有的定量方法是有限的，并且提供了材料中所有磁性颗粒的整体表征，而不是关于单个矿物成分的诊断信息。该项目旨在开发一个机器学习框架，对单个天然样品中的每一种磁性矿物成分进行分解和量化，并将开启岩石磁性的新量化时代。预计它将通过在物理、材料科学和工业中实现磁性表征，产生超出地球科学的影响。