In the modern oceans, the relative abundances of glycerol dialkyl glycerol tetraether (GDGT) compounds produced by marine archaeal communities show a significant dependence on the local sea surface temperature at the site of deposition. When preserved in ancient marine sediments, the measured abundances of these fossil lipid biomarkers thus have the potential to provide a geological record of long-term variability in planetary surface temperatures. Several empirical calibrations have been made between observed GDGT relative abundances in late Holocene core-top sediments and modern upper ocean temperatures. These calibrations form the basis of the widely used TEX₈₆ palaeothermometer. There are, however, two outstanding problems with this approach: first the appropriate assignment of uncertainty to estimates of ancient sea surface temperatures based on the relationship of the ancient GDGT assemblage to the modern calibration dataset, and second, the problem of making temperature estimates beyond the range of the modern empirical calibrations (> 30 ^∘C). Here we apply modern machine learning tools, including Gaussian process emulators and forward modelling, to develop a new mathematical approach we call OPTiMAL (Optimised Palaeothermometry from Tetraethers via MAchine Learning) to improve temperature estimation and the representation of uncertainty based on the relationship between ancient GDGT assemblage data and the structure of the modern calibration dataset. We reduce the root mean square uncertainty on temperature predictions (validated using the modern dataset) from ∼ ±6 ^∘C using TEX₈₆-based estimators to ±3.6 ^∘C using Gaussian process estimators for temperatures below 30 ^∘C. We also provide a new quantitative measure of the distance between an ancient GDGT assemblage and the nearest neighbour within the modern calibration dataset, as a test for significant non-analogue behaviour.

中文翻译：

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OPTiMAL：基于GDGT的古温度计的新机器学习方法

在现代海洋中，海洋古生菌群落产生的甘油二烷基甘油四醚（GDGT）化合物的相对丰度表现出对沉积地点局部海表温度的显着依赖性。当保存在古代海洋沉积物中时，这些化石脂质生物标志物的丰度因此可能提供行星表面温度长期变化的地质记录。在晚全新世岩心顶沉积物中观测到的GDGT相对丰度与现代高洋温度之间进行了一些经验校准。这些校准构成了广泛使用的TEX ₈₆的基础古温度计。但是，这种方法存在两个突出的问题：首先，根据古代GDGT组合与现代标定数据集之间的关系，对古代海面温度的估计值进行适当的不确定性分配；其次，使温度估计值超出范围的问题。现代经验校准的范围（>  30  ^∘C）。在这里，我们运用现代的机器学习工具，包括高斯过程仿真器和正向建模，来开发一种新的数学方法，我们称之为OPTiMAL（通过机器学习从四醚中获得最佳的古热计量法）来改进温度估算和基于古代GDGT之间关系的不确定性表示组装数据和现代校准数据集的结构。我们减少根上的温度预测均方误差（利用现代数据集验证）从 〜 ± 6  ^∘使用TEXÇ ₈₆基于估计到± 3.6  ^∘下使用高斯过程估计温度低于30  ^∘C.我们还提供了一种新的定量方法，用于测量古代GDGT组件与现代校准数据集中最近的邻居之间的距离，以测试重要的非类似行为。