EAGER: Development of a Multivariate Biomarker Analysis Technique for Paleoclimate Reconstruction

基于化学“代用品”对过去环境的重建，为了解地球系统在从几十年到几百万年的各种时间尺度上如何运作提供了宝贵的见解。有机分子代用指标，又称“生物标志物”代用指标，在古气候研究中的应用日益广泛，这些代用指标包含了大量的环境信息，包括温度、干旱度、植被类型和水体盐度等。然而，他们目前的解释主要是在一个单一的变量的利益，最常见的温度。这项研究由伍兹霍尔海洋研究所的一位早期职业研究人员领导，是首次尝试利用生物标志物数据的多维质量进行古气候重建。将使用人工神经网络和支持向量回归算法（“机器学习”技术）建立统计模型。这将允许定量评价沉积档案中生物标志物组合与一系列现代环境条件的关系。拟议项目的成果将包括开发一个新的工具箱，以便在许多类型的古气候档案中定量重建陆地和海洋过去的气候。这样的工具可以在包括环境研究、石油地质学和计算机科学在内的一系列领域中具有广泛的应用。

Reconstructions of past environments based on chemical "proxies" have provided invaluable insights into how the Earth system functions on a variety of timescales, from decades to millions of years. Organic molecular, or "biomarker", proxies are being applied increasingly in paleoclimate studies, and these compounds hold a wealth of information about an array of environmental conditions includin temperature, aridity, vegetation biome type, and water salinity. However, their current interpretation is mainly in terms of a single variable of interest, most often temperature. This study, led by an early-career researcher from the Woods Hole Oceanographic Institution, is a first attempt to leverage the multidimensional quality of biomarker data for paleoclimate reconstruction. A statistical model will be developed using artificial neural network (ANN) and support vector regression (SVR) algorithms ("machine learning" techniques). This will allow quantitative evaluation of how assemblages of biomarkers in sedimentary archives relate to an array of modern environmental conditions. The outcomes of the proposed project will include development of a new toolbox to quantitatively reconstruct past climates over land and in the ocean in many types of paleoclimate archives. Such a tool could have broad application across a range of fields including environmental studies, petroleum geology, and computer science.