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

EAGER：开发用于古气候重建的多变量生物标志物分析技术

• 批准号: 1443176
• 负责人: Jessica Tierney
• 金额: $ 7.59万
• 依托单位: Woods Hole Oceanographic Institution
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-15 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443176&HistoricalAwards=false
• 关键词: EAGER; Development; Multivariate; Biomarker; Analysis

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.

基于化学“代用物”对过去环境的重建，为了解地球系统在从几十年到数百万年的各种时间尺度上是如何运作的提供了宝贵的见解。有机分子或“生物标志物”代用物在古气候研究中得到越来越多的应用，这些化合物含有关于一系列环境条件的丰富信息，包括温度、干旱、植被生物群系类型和水盐度。然而，他们目前的解释主要是根据一个感兴趣的单一变量，最常见的是温度。这项研究由伍兹霍尔海洋研究所的一位早期职业研究员领导，是利用生物标志物数据的多维质量进行古气候重建的第一次尝试。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。这将允许定量评估沉积档案中生物标志物的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，在许多类型的古气候档案中定量重建过去的陆地和海洋气候。这样的工具可以广泛应用于包括环境研究、石油地质学和计算机科学在内的一系列领域。