Comparing global seismic tomography models using varimax principal component analysis
Comparing global seismic tomography models using varimax principal component analysis
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DOI：
10.5194/se-12-1601-2021
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发表时间：
2021-07
期刊：
影响因子：
3.4
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
摘要。全球地震层析成像在过去几十年里取得了很大进展，不同的研究小组制作了许多全球地球模型。客观地说，统计方法对于模型所包含的大量信息的定量解释和无偏模型比较至关重要。为了简化地质解释和模型比较，我们建议使用旋转主成分分析（PCA）来压缩信息。该方法为七个测试的全局层析模型中的每个模型生成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，通过投影与一个水平模式相关联。深度轮廓和水平模式可以检查模型主要组件的关键特征。模型中的大部分信息与以下几个特征有关：最下部地幔中的大低剪切速度省（llsvp），上、下地幔中可能与地幔柱有关的俯冲信号和低速异常，以及最上部地幔中的脊和克拉通。重要的是，所有模型都强调了下地幔中几个独立的分量，这些分量占总方差的36%到69%，这取决于模型，这表明下地幔比传统假设的要复杂得多。总的来说，我们发现变差主成分分析是定量比较和解释断层扫描模型的一个有用的附加工具。
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 and for unbiased model comparisons. Here we propose using a rotated version of principal component analysis (PCA) to compress the information in order to ease the geological interpretation and model comparison. The method generates between 7 and 15 principal components (PCs) 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, with 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 varimax PCA is a useful additional tool for the quantitative comparison and interpretation of tomography models.