Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics (e.g., artificial intelligence, machine learning, data modeling) into the geochemical survey data pipeline occurs almost entirely within the data usage stage. In this study, we predict elemental concentrations at the data generation stage and explore how predictive analytics can be integrated more thoroughly across the data lifecycle. Inferential data generation is used to modernize lake sediment geochemical data from northern Manitoba (Canada), with results and interpretations focused on elements that are included in the Canadian Critical Minerals list. The results are mapped, interpreted and used for downstream analysis through geochemical anomaly detection to locate further exploration targets. Our integration is novel because predictive modeling is integrated into the data generation and usage stages to increase the efficacy of geochemical surveys. The results further demonstrate how legacy geochemical data are a significant data asset that can be predictively modernized and used to support time-sensitive mineral exploration of critical minerals that were unanalyzed in original survey designs. In addition, this type of integration immediately creates the possibility of a new exploration framework, which we call predictive geochemical exploration. In effect, it eschews sequential, grid-based and fixed resolution sampling toward data-driven, multi-scale and more agile approaches. A key outcome is a natural categorization scheme of uncertainty associated with further survey or exploration targets, whether they are covered by existing training data in a spatial or multivariate sense or solely within the coverage of inferred secondary data. The uncertainty categorization creates an effective implementation pathway for future multi-scale exploration by focusing data generation activities to de-risk survey practices.

地球化学调查包含隐​​含的数据生命周期或管道，其中包括数据生成（例如，采样和分析）、数据管理（例如，质量保证和控制、管理、供应和管理）和数据使用（例如，绘图、建模和假设检验） ）。当前预测分析（例如人工智能、机器学习、数据建模）与地球化学调查数据管道的集成几乎完全发生在数据使用阶段。在本研究中，我们预测数据生成阶段的元素浓度，并探索如何在整个数据生命周期中更彻底地集成预测分析。推断数据生成用于对马尼托巴省北部（加拿大）的湖泊沉积物地球化学数据进行现代化改造，其结果和解释重点关注加拿大关键矿物清单中包含的元素。结果被绘制、解释并用于通过地球化学异常检测进行下游分析，以定位进一步的勘探目标。我们的集成是新颖的，因为预测模型被集成到数据生成和使用阶段，以提高地球化学调查的效率。结果进一步证明了遗留地球化学数据如何成为重要的数据资产，可以进行预测性现代化，并用于支持对原始勘测设计中未分析的关键矿物进行时间敏感的矿物勘探。此外，这种类型的整合立即创造了新的勘探框架的可能性，我们称之为预测地球化学勘探。实际上，它避开了顺序、基于网格和固定分辨率的采样，转而采用数据驱动、多尺度和更敏捷的方法。一个关键成果是与进一步调查或勘探目标相关的不确定性的自然分类方案，无论它们是在空间或多变量意义上的现有训练数据所覆盖，还是仅在推断的二级数据的覆盖范围内。不确定性分类通过集中数据生成活动来降低调查实践的风险，为未来的多尺度勘探创造了有效的实施途径。