Comparing global seismic tomography models using the varimax Principal Component Analysis
Comparing global seismic tomography models using the varimax Principal Component Analysis
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DOI：
10.5194/se-2021-16
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
O. de Viron;M. Van Camp;A. Grabkowiak;A. Ferreira
中科院分区：
文献类型：
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作者：
O. de Viron;M. Van Camp;A. Grabkowiak;A. Ferreira
抽象的。全球地震层析成像在过去的几十年里取得了很大的进展，不同的研究小组制作了许多全球地球模型。客观的统计方法对于定量解释模型所包含的大量信息以及无偏模型比较至关重要。在这里，我们建议使用主成分分析（PCA）的旋转版本来压缩信息，以便于地质解释和模型比较。该方法为七个测试的全局层析成像模型中的每一个生成7到15个主成分（PC），捕获模型总方差的97%以上。每个PC由一个垂直剖面组成，通过投影将一个水平图案与该垂直剖面相关联。深度剖面图和水平模式使我们能够检查模型主要组成部分的关键特征。模型中的大部分信息与一些特征相关：最低地幔中的大低剪切速度区（LLSVP），俯冲信号和低速异常可能与上地幔和下地幔中的地幔柱有关，以及上地幔中的脊和脊。重要的是，所有模型都突出了下地幔中的几个独立成分，这些成分占总方差的36%至69%，这取决于模型，这表明下地幔比传统假设更复杂。总的来说，我们发现方差最大PCA是一个有用的额外的工具，定量比较和解释的层析成像模型。
Abstract. Global seismic tomography has greatly progressed in the past decades, with many global Earth models being produced by different research groups. Objective, statistical methods are crucial for the quantitative interpretation of the large amount of information encapsulated by the models as well as for unbiased model comparisons. We propose here to use a rotated version of the Principal Component Analysis (PCA) to compress the information, in order to ease the geological interpretation and model comparison. The method generates between 7 to 15 principal components (PC) for each of the seven tested global tomography models, capturing more than 97 % of the total variance of the model. Each PC consists of a vertical profile, to which a horizontal pattern is associated by projection. The depth profiles and the horizontal patterns enable examining the key characteristics of the main components of the models. Most of the information in the models is associated with a few features: Large Low Shear Velocity Provinces (LLSVPs) in the lowermost mantle, subduction signals and low velocity anomalies likely associated with mantle plumes in the upper and lower mantle, and ridges and cratons in the uppermost mantle. Importantly, all models highlight several independent components in the lower mantle that make between 36 % and 69 % of the total variance, depending on the model, which suggests that the lower mantle is more complex than traditionally assumed. Overall, we find that the varimax PCA is a useful additional tool for the quantitative comparison and interpretation of tomography models.