Developing a statistical approach to analyze large paired geo-thermochronological datasets with an application to the Canadian Cordilleras

开发一种统计方法来分析大型配对地质热年代学数据集并应用于加拿大科迪勒拉山脉

• 批准号: 439621066
• 负责人: Dr. Tobias Stephan
• 金额: --
• 依托单位: Department of Geoscience
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/439621066?language=en
• 关键词: Developing; statistical; approach; analyze; large

Laser ablation U–Pb dating of detrital minerals is the most common approach in provenance analyses. The method allows producing large geochronological datasets in a fast and cost efficient manner as wells as the application of several different dating methods such as U–Pb, (U–Th)/He, and fission track on the same detrital grain. The combination of geochronological and thermochronological methods provides valuable information for the understanding the coupling between tectonic processes, erosion, sedimentary transport and recycling, and paleo-climate.The increase of age data complicates the data analysis and thus the interpretation. The combination of several different provenance information, in particular the use of multi-method ages from single grains, expands the dataset by one or more dimensions. The multivariate analysis of more than one provenance proxy is complex and mostly done by analyzing the individual proxies separately. This approach, however, does not exhaust the entire potential of multi-method provenance analysis as it ignores the initial linkage of the different information of each individual grain and, thus, erroneously assumes that the different information represent independent variables. The overarching objective of this project is to develop a statistical procedure for quantitative provenance analysis using a large amount of samples analyzed by multi-method dating. By applying modern statistical methods such as Bayesian inference, dimensionality reduction and clustering, the procedure will include quantification of the dissimilarities of the samples, identification of the provenance endmember, quantification of sedimentary mixing, and prediction of provenance of single-method age datasets. In order to guarantee geological meaningful results, a priori assumptions on the multi-method dataset have to be made in the first place. This step will include data pre-processing, bias, and noise reduction. The numeric quantification of the pairwise dissimilarities of multi-method dated detrital sample will be pursued by testing two approaches: (i) non-negative matrix factorization and (ii) trans-dimensional Bayesian mixture modelling applying hierarchical models for noise estimation. Provenance endmember and sedimentary mixing will be identified and quantified by exploratory data analysis, in particular multidimensional scaling and density-based clustering. The statistic procedure will be tested on synthetically generated datasets and double- and triple dated detritus from the Canadian Cordilleras.The new approach can be easily applied to other paired radio-isotopic chronometers, e.g. U–Pb and Lu–Hf dating, which offers a large potential for future geo- and thermochronological studies. Moreover, it will provide robust results for quantitative provenance analysis that are required to understand the complex coupling between endogenous and exogenous processes.

碎屑矿物激光剥蚀U-Pb定年是物源分析中最常用的方法。该方法允许以快速和成本有效的方式产生大的地质年代学数据集，如威尔斯作为几种不同的定年方法，如U-Pb，（U-Th）/He，和裂变径迹上相同的碎屑颗粒的应用。年代学和热年代学的结合为认识构造作用、侵蚀作用、沉积物搬运和再循环与古气候的耦合提供了有价值的信息，但年代学数据的增加使数据分析和解释复杂化。几种不同的来源信息的组合，特别是使用多方法年龄从单一的颗粒，扩大了一个或多个维度的数据集。多个种源代理的多变量分析是复杂的，并且主要通过单独分析各个代理来完成。然而，这种方法并没有穷尽多方法种源分析的全部潜力，因为它忽略了每个单独颗粒的不同信息的初始联系，因此错误地假设不同的信息代表独立变量。该项目的总体目标是开发一种统计程序，用于使用多方法测年分析的大量样品进行定量物源分析。通过应用贝叶斯推断、降维和聚类等现代统计方法，该程序将包括量化样品的不同性、识别物源端元、量化沉积混合以及预测单一方法年龄数据集的物源。为了保证地质上有意义的结果，必须首先对多方法数据集进行先验假设。这一步骤将包括数据预处理、偏差和降噪。将通过测试两种方法对多方法定年碎屑样本的成对不相似性进行数值量化：（i）非负矩阵分解和（ii）应用分层模型进行噪声估计的跨维贝叶斯混合建模。将通过探索性数据分析，特别是多维标度和基于密度的聚类，确定和量化物源端元和沉积混合。该方法将在加拿大科迪勒拉山脉的碎屑岩和合成数据集上进行验证，并可应用于其它同位素年代学研究，如U-Pb和Lu-Hf年代学，为今后的地质年代学和热年代学研究提供了巨大的潜力。此外，它将提供可靠的结果，定量分析所需的了解内源性和外源性过程之间的复杂耦合的起源。