Ernst David M, Vogt Joachim, Bau Michael, Mues Malte
Critical Metals for Enabling Technologies - CritMET, School of Science, Constructor University, Campus Ring 1, 28759, Bremen, Germany.
School of Science, Constructor University, Campus Ring 1, 28759, Bremen, Germany.
Sci Rep. 2025 Feb 13;15(1):5360. doi: 10.1038/s41598-025-89227-2.
Rare earth elements (REEs) are powerful proxies used in many (bio-)geochemical studies. Interpretation of REE data relies on normalised REE patterns and anomaly quantification, and requires complete data. Therefore, older, high-quality REE data determined by neutron activation or isotope dilution methods are often ignored, as they did not provide complete data. Similarly, modern analytical data can lack certain REEs due to quantification limits, interferences or usage of REE spikes. However, such data may be the only information available since sample material was consumed, sample locations became inaccessible, or samples represent past states of a dynamic natural system. Therefore, the ability to impute such high-quality data is of value for many geoscientific sub-disciplines. We use a polynomial modelling approach to impute missing REE data, verify the method's applicability with a large data set (>13,000 samples; PetDB), and complement three originally incomplete REE data sets. Good fitting results (SD <6%) are supported by Monte Carlo simulations for assessing the model uncertainties (± 12%). Additionally, we provide a procedure to quantify REE anomalies, including uncertainties, which were usually not determined in the past but are essential for scientific comparison of REE anomaly data between different data sets. All Python scripts are provided.
稀土元素（REEs）是许多（生物）地球化学研究中使用的有力代理指标。稀土元素数据的解释依赖于标准化的稀土元素模式和异常量化，并且需要完整的数据。因此，通过中子活化或同位素稀释法测定的年代较久的高质量稀土元素数据常常被忽视，因为它们没有提供完整的数据。同样，由于量化限制、干扰或稀土元素标样的使用，现代分析数据可能缺少某些稀土元素。然而，由于样品材料已消耗、样品位置无法到达或样品代表动态自然系统的过去状态，此类数据可能是唯一可用的信息。因此，估算此类高质量数据的能力对许多地球科学子学科都具有价值。我们使用多项式建模方法来估算缺失的稀土元素数据，用一个大数据集（>13000个样品；PetDB）验证该方法的适用性，并补充三个原本不完整的稀土元素数据集。蒙特卡罗模拟支持了良好的拟合结果（标准差<6%），用于评估模型不确定性（±12%）。此外，我们提供了一种量化稀土元素异常的程序，包括不确定性，过去通常未确定这些不确定性，但它们对于不同数据集之间稀土元素异常数据的科学比较至关重要。所有Python脚本均已提供。
