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

根据化学“替代物”重建过去的环境，为地球系统在从几十年到数百万年的各种时间尺度上如何运作提供了宝贵的见解。有机分子或“生物标志物”正越来越多地被应用于古气候研究，这些化合物包含关于一系列环境条件的丰富信息，包括温度、干旱、植被生物群类型和水的盐度。然而，他们目前的解释主要是关于感兴趣的单一变量，最常见的是温度。这项研究由伍兹霍尔海洋研究所的一位职业生涯早期的研究人员领导，是利用生物标志物数据的多维质量来重建古气候的首次尝试。将使用人工神经网络(ANN)和支持向量回归(SVR)算法(“机器学习”技术)开发统计模型。这将使定量评估沉积档案中的生物标志物组合如何与一系列现代环境条件相关。拟议项目的成果将包括开发一个新的工具箱，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、石油地质和计算机科学在内的一系列领域得到广泛应用。

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.