Characterizing sediment sources by non-negative matrix factorization of detrital geochronological data
Characterizing sediment sources by non-negative matrix factorization of detrital geochronological data
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DOI：
10.1016/j.epsl.2019.01.044
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发表时间：
2019-04
影响因子：
5.3
通讯作者：
J. Saylor;K. Sundell;G. Sharman
中科院分区：
文献类型：
--
作者：
J. Saylor;K. Sundell;G. Sharman
本文探索了一种逆向方法来解决基于特定沉积中心的样本（“汇”样本）使用非负矩阵分解（NMF）来表征沉积物源（“源”样本）年龄分布的问题。它还概述了一种确定从一组接收器样本中分解的最佳源数量（即最佳分解等级）的方法。我们通过将接收样本生成为已知源的随机混合物、对它们进行因式分解并恢复已知源的数量、它们的年龄分布以及用于生成接收样本的加权函数来证明该方法的强大功能。敏感性测试表明，分解源和已知源之间的相似性与 1) 汇样本数量、2) 汇样本之间的差异性以及 3) 汇样本大小呈正相关。具体来说，当接收器样本数量超过源样本数量的 3 倍、接收器数据集内部不相似（互相关系数范围 >0.3、KuiperV 值范围 >0.35）并且接收器样本具有良好特征（>150-225 个数据点）时，该算法会在分解源和已知源之间产生一致、密切的相似性。然而，如果其他变量增加，则已知源和因式分解源之间的相似性可以保持，同时减少其中一些变量。对来自布克悬崖、大峡谷和墨西哥湾的三个经验碎屑锆石 U-Pb 数据集进行因式分解，得出合理的源年龄分布和权重。 Book Cliffs 数据集的分解产生了五个与最近独立提出的 Book Cliffs 地层主要来源非常相似的来源；确认 NMF 方法的实用性。 Grand Canyon 数据集举例说明了应用 NMF 算法时的两个一般考虑因素。首先，虽然 NMF 算法能够识别源年龄分布，但需要额外的地质细节来区分原始源或回收源。其次，NMF 算法将识别混合汇样本的最基本元素，因此可以将本身是更多基本元素的异质混合物的源细分为这些基本元素。最后，对墨西哥湾大型数据集的应用突显了白垩纪和全新世时期阿巴拉契亚水源的贡献增加，这可能归因于排水重组。尽管该算法再现了已知来源并为经验数据集产生了合理来源，但反演本质上是不唯一的。因此，NMF 的结果及其解释应根据独立的地质证据进行评估。 NMF 算法以 MATLAB 代码和适用于 Windows 和 macOS（.exe 和 .app）的独立图形用户界面以及本贡献中讨论的所有数据集的形式提供。
This paper explores an inverse approach to the problem of characterizing sediment sources' (“source” samples) age distributions based on samples from a particular depocenter (“sink” samples) using non-negative matrix factorization (NMF). It also outlines a method to determine the optimal number of sources to factorize from a set of sink samples (i.e., the optimum factorization rank). We demonstrate the power of this method by generating sink samples as random mixtures of known sources, factorizing them, and recovering the number of known sources, their age distributions, and the weighting functions used to generate the sink samples. Sensitivity testing indicates that similarity between factorized and known sources is positively correlated to 1) the number of sink samples, 2) the dissimilarity among sink samples, and 3) sink sample size. Specifically, the algorithm yields consistent, close similarity between factorized and known sources when the number of sink samples is more than ∼3 times the number of source samples, sink data sets are internally dissimilar (cross-correlation coefficient range >0.3, KuiperVvalue range >0.35), and sink samples are well-characterized (>150–225 data points). However, similarity between known and factorized sources can be maintained while decreasing some of these variables if other variables are increased.Factorization of three empirical detrital zircon U–Pb data sets from the Book Cliffs, the Grand Canyon, and the Gulf of Mexico yields plausible source age distributions and weights. Factorization of the Book Cliffs data set yields five sources very similar to those recently independently proposed as the primary sources for Book Cliffs strata; confirming the utility of the NMF approach. The Grand Canyon data set exemplifies two general considerations when applying the NMF algorithm. First, although the NMF algorithm is able to identify source age distribution, additional geological details are required to discriminate between primary or recycled sources. Second, the NMF algorithm will identify the most basic elements of the mixed sink samples and so may subdivide sources that are themselves heterogeneous mixtures of more basic elements into those basic elements. Finally, application to a large Gulf of Mexico data set highlights the increased contribution from Appalachian sources during Cretaceous and Holocene time, potentially attributable to drainage reorganization. Although the algorithm reproduces known sources and yields reasonable sources for empirical data sets, inversions are inherently non-unique. Consequently, the results of NMF and their interpretations should be evaluated in light of independent geological evidence. The NMF algorithm is provided both as MATLAB code and a stand-alone graphical user interface for Windows and macOS (.exe and .app) along with all data sets discussed in this contribution.