Abstract Numerous parameters impact apatite (U-Th-Sm)/He (AHe) thermochronological dates, such as radiation damage, chemical content, crystal size and geometry, and their knowledge is essential for better geological interpretations. The present study investigates a new method based on advanced data mining techniques, to unravel the parameters that could play a role in He retention and thus on AHe date. The purpose is to decipher which factors influence the AHe date dispersion, and to exclude the impact of other parameters on helium retention. As an example, we use a dataset previously collected on apatite from basements rocks, sampled in French Brittany, where all samples underwent the same thermal history, and for which were reported a set of physical and chemical parameters. The dataset includes dimension and geometry, He, U, Th, Sm and major and trace element content for ∼35 crystals. The algorithm ranks the parameters according to their influence on helium retention, using predictive trees, which are commonly used in computing sciences. After looking at 100 simultaneous predictions, we compared the predicted and measured He content for each analyzed apatite crystal. For this particular case, the predictions confirmed the prominent role of the parent nuclides in the He production, as AHe dates can be predicted accurately with these parameters (especially U and Th). Additionally, the predictions without knowledge of the apatite chemical composition and dimension provided better results than using all available parameters (median error of 14% instead of 18%). Therefore, for this specific study, the apatite chemistry and crystal dimensions do not influence significantly He retention nor AHe date dispersion. Nevertheless, detailed inspection of analysis results suggests which parameters have the most discrimination ability, which in this study include crystal length, height, and Mn content. The latter may reveal an eventual influence on alpha damage annealing kinetics. Finally, this approach shows that some grains could never achieve good predictions, indicating that for these crystals the input parameters are not enough to predict the He content. We propose that such crystals are statistically different from the remaining dataset, and this suggests that machine learning has a strong potential to correct errors, or to detect anomalies.

中文翻译：

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磷灰石 (U-Th-Sm)/He 日期色散：来自机器学习算法的初步见解

摘要 许多参数影响磷灰石 (U-Th-Sm)/He (AHe) 热年代学日期，例如辐射损伤、化学成分、晶体尺寸和几何形状，它们的知识对于更好的地质解释至关重要。本研究调查了一种基于先进数据挖掘技术的新方法，以揭示可能在 He 保留以及 AHe 日期中起作用的参数。目的是破译影响 AHe 日期分散的因素，并排除其他参数对氦保留的影响。例如，我们使用先前从基底岩石的磷灰石上收集的数据集，在法国布列塔尼采样，所有样品都经历了相同的热历史，并报告了一组物理和化学参数。数据集包括维度和几何，He，U，Th，～35 个晶体的 Sm 和主要和微量元素含量。该算法使用计算科学中常用的预测树，根据参数对氦保留的影响对参数进行排名。在查看了 100 个同时预测后，我们比较了每个分析的磷灰石晶体的预测和测量 He 含量。对于这种特殊情况，预测证实了母核素在 He 生产中的突出作用，因为可以使用这些参数（尤其是 U 和 Th）准确预测 AHe 日期。此外，与使用所有可用参数相比，在不了解磷灰石化学成分和尺寸的情况下进行的预测提供了更好的结果（中值误差为 14% 而不是 18%）。因此，对于这项具体的研究，磷灰石化学性质和晶体尺寸不会显着影响 He 保留和 AHe 日期分散。尽管如此，对分析结果的详细检查表明哪些参数具有最大的辨别能力，在本研究中包括晶体长度、高度和 Mn 含量。后者可能会揭示对 alpha 损伤退火动力学的最终影响。最后，这种方法表明某些晶粒永远无法实现良好的预测，表明对于这些晶体，输入参数不足以预测 He 含量。我们提出这种晶体在统计上与其余数据集不同，这表明机器学习具有纠正错误或检测异常的强大潜力。对分析结果的详细检查表明哪些参数具有最大的辨别能力，在本研究中包括晶体长度、高度和 Mn 含量。后者可能会揭示对 alpha 损伤退火动力学的最终影响。最后，这种方法表明某些晶粒永远无法实现良好的预测，表明对于这些晶体，输入参数不足以预测 He 含量。我们提出这种晶体在统计上与其余数据集不同，这表明机器学习具有纠正错误或检测异常的强大潜力。对分析结果的详细检查表明哪些参数具有最大的辨别能力，在本研究中包括晶体长度、高度和 Mn 含量。后者可能会揭示对 alpha 损伤退火动力学的最终影响。最后，这种方法表明某些晶粒永远无法实现良好的预测，表明对于这些晶体，输入参数不足以预测 He 含量。我们提出这种晶体在统计上与其余数据集不同，这表明机器学习具有纠正错误或检测异常的强大潜力。这种方法表明，一些晶粒永远无法实现良好的预测，表明对于这些晶体，输入参数不足以预测 He 含量。我们提出这种晶体在统计上与其余数据集不同，这表明机器学习具有纠正错误或检测异常的强大潜力。这种方法表明，一些晶粒永远无法实现良好的预测，表明对于这些晶体，输入参数不足以预测 He 含量。我们提出这种晶体在统计上与其余数据集不同，这表明机器学习具有纠正错误或检测异常的强大潜力。