Predictive grids of major oxide concentrations in surface rock and regolith over the Australian continent

Major oxides provide valuable information about the composition, origin, and properties of rocks and regolith. Analysing major oxides contributes significantly to understanding the nature of geological materials and processes (i.e. physical and chemical weathering) – with potential applications in resource exploration, engineering, environmental assessments, agriculture, and other fields. Traditionally most measurements of oxide concentrations are obtained by laboratory assay, often using X-ray fluorescence, on rock or regolith samples. To expand beyond the point measurements of the geochemical data, we have used a machine learning approach to produce seamless national scale grids for each of the major oxides. This approach builds predictive models by learning relationships between the site measurements of an oxide concentration (sourced from Geoscience Australia’s OZCHEM database and selected sites from state survey databases) and a comprehensive library of covariates (features). These covariates include: terrain derivatives; climate surfaces; geological maps; gamma-ray radiometric, magnetic, and gravity grids; and satellite imagery. This approach is used to derive national predictions for 10 major oxide concentrations at the resolution of the covariates (nominally 80 m). The models include the oxides of silicon (SiO2), aluminium (Al2O3), iron (Fe2O3tot), calcium (CaO), magnesium (MgO), manganese (MnO), potassium (K2O), sodium (Na2O), titanium (TiO2), and phosphorus (P2O5). The grids of oxide concentrations provided include the median of multiple models run as the prediction, and lower and upper (5th and 95th) percentiles as measures of the prediction’s uncertainty. Higher uncertainties correlate with greater spreads of model values. Differences in the features used in the model compared with the full feature space covering the entire continent are captured in the ‘covariate shift’ map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful, when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map. Details of the modelling approach, model uncertainties and datasets are describe in an attached word document “Model approach uncertainties”. This work is part of Geoscience Australia’s Exploring for the Future program that provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. These data are published with the permission of the CEO, Geoscience Australia.

主量氧化物（Major oxides）可为岩石与风化层（regolith）的组成、成因及属性提供极具价值的信息。对主量氧化物开展分析，对理解地质物质与地质作用（即物理风化与化学风化过程）的本质具有重要意义，其潜在应用场景涵盖资源勘探、工程建设、环境评估、农业等诸多领域。传统意义上，绝大多数氧化物浓度的测量均通过实验室化验完成，通常采用X射线荧光光谱法（X-ray fluorescence），分析对象为岩石或风化层样本。为突破地球化学数据仅能获取点位测量值的局限，我们采用机器学习方法，生成了覆盖全国范围的各主量氧化物无缝格网数据集。该方法通过学习氧化物浓度的野外点位测量值（数据源自澳大利亚地质调查局（Geoscience Australia）的OZCHEM数据库，以及州级调查数据库筛选的采样点位）与涵盖各类协变量（特征）的综合库之间的关联关系，构建预测模型。此类协变量包括：地形衍生数据、气候表面数据集、地质图件、伽马射线放射性、磁法与重力格网数据，以及卫星影像。本方法被用于生成10种主量氧化物浓度的全国预测格网，分辨率与协变量格网保持一致，标称分辨率为80米。所涉及的氧化物包括：硅（SiO₂）、铝（Al₂O₃）、全铁（Fe₂O₃tot）、钙（CaO）、镁（MgO）、锰（MnO）、钾（K₂O）、钠（Na₂O）、钛（TiO₂）以及磷（P₂O₅）。本次提供的氧化物浓度格网数据，包含以多模型预测的中位数作为最终预测结果，同时附带5th与95th百分位数作为预测不确定性的衡量指标。不确定性越高，对应模型预测值的离散程度越大。相较于覆盖整个澳洲大陆的完整特征空间，模型中所使用的特征差异可通过“协变量偏移（covariate shift）”图进行体现。偏移模型中的高值区域，往往预示着模型预测的不确定性更高或可靠性更差。因此，用户在解读本数据集时，需格外留意5th至95th百分位数所展示的不确定性水平，以及协变量偏移图中的高值区域。关于建模方法、模型不确定性及数据集的详细信息，已收录于附件Word文档"Model approach uncertainties"（建模方法与不确定性）。本研究属于澳大利亚地质调查局（Geoscience Australia）“探索未来（Exploring for the Future）”计划的组成部分，该计划旨在提供预竞争性地质科学信息，为政府、社区与行业在澳大利亚矿产、能源与地下水资源的可持续开发决策中提供支撑。通过收集、分析与解读新增及现有的预竞争性地质科学数据与相关知识，我们正逐步构建起澳大利亚地质状况与资源潜力的全国性图景。这一工作将助力打造强劲的经济、富有韧性的社会与可持续的环境，惠及全体澳大利亚民众。其中包括支持澳大利亚向净零排放转型，推动稳健可持续的资源与农业产业发展，并为澳大利亚偏远与内陆社区创造经济机遇与社会福祉。“探索未来”计划始于2016年，是澳大利亚政府投入2.25亿澳元、为期8年的国家级投资项目。本数据集经澳大利亚地质调查局首席执行官批准后发布。