OPTiMAL: a new machine learning approach for GDGT-based palaeothermometry
OPTiMAL: a new machine learning approach for GDGT-based palaeothermometry
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DOI：
10.5194/cp-2019-60
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发表时间：
2019-06
影响因子：
4.3
通讯作者：
T. Dunkley Jones;Y. Eley;W. Thomson;S. Greene;I. Mandel;K. Edgar;J. Bendle
中科院分区：
文献类型：
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作者：
T. Dunkley Jones;Y. Eley;W. Thomson;S. Greene;I. Mandel;K. Edgar;J. Bendle
抽象的。在现代海洋中，海洋古细菌群落产生的甘油二烷基甘油四醚（GDGT）化合物的相对丰度显示出对沉积地点的当地海面温度的显着依赖。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测量丰度因此有可能提供行星表面温度长期变化的地质记录。在晚全新世岩芯顶部沉积物中观测到的GDGT相对丰度与现代上层海洋温度之间进行了几次经验校准。这些刻度构成了广泛使用的TEX 86古温度计的基础。然而，这种方法有两个突出的问题：第一，根据古代GDGT组合与现代校准数据集的关系，对古代海面温度的估计值进行适当的不确定性分配;第二，温度估计值超出现代经验校准范围（> 30 ° C）的问题。在这里，我们应用现代机器学习工具，包括高斯过程仿真器和正向建模，开发一种新的数学方法，我们称之为OPTIMAL（通过MAchine学习从四醚优化古温度测量法），以改善温度估计和基于古代GDGT组合数据与现代校准数据集结构之间的关系的不确定性表示。我们将温度预测的均方根不确定性（使用现代数据集进行验证）从使用基于TEX 86的估计器的±6 ° C降低到使用高斯过程估计器的±3.6 ° C（温度低于30 ° C）。我们还提供了一个新的定量测量的距离之间的古代GDGT组合和最近的邻居在现代校准数据集，作为一个测试显着的非模拟行为。
Abstract. 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.