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首页> 外文期刊>Earth and Planetary Science Letters: A Letter Journal Devoted to the Development in Time of the Earth and Planetary System >Short scale heterogeneity in the lowermost mantle: insights from PcP-P and ScS-S data
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Short scale heterogeneity in the lowermost mantle: insights from PcP-P and ScS-S data

机译：最下层地幔的短尺度异质性：来自PcP-P和ScS-S数据的见解

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摘要

We compare lateral variations at the base of the mantle as inferred from a global dataset of PcP-P travel time residuals, measured on broadband records, and existing P and S tomographic velocity models, as well as ScS-S travel time data in some selected regions. In many regions, the PcP-P dataset implies short scale lateral variations that are not resolved by global tomographic models, except under eastern Eurasia, where data and models describe a broad region of fast velocity anomalies across which variations appear to be of thermal origin. In other regions, such as central America and southeastern Africa, correlated short scale lateral variations (several hundred kilometers) are observed in PcP and ScS, implying large but not excessive values for the ratio R = (partial deriv)ln V_s/(partial deriv)ln V_p (～2.5). On the other hand, in at least two instances, in the heart of the African Plume and on the edge of the Pacific Plume, variations in P and S velocities appear to be incompatible, implying strong lateral gradients across compositionally different domains, possibly also involving topography on the core-mantle boundary. One should be cautious in estimating R at the base of the mantle from global datasets, as different smoothing and sampling of P and S datasets may result in strong biases and meaningless results.
机译：我们根据在宽带记录上测得的PcP-P行进时间残差的全球数据集以及现有的P和S层析速度模型以及某些选定的ScS-S行进时间数据推断出的地幔底部横向变化地区。在许多地区，PcP-P数据集暗示了全球层析成像模型无法解决的短尺度横向变化，但在东亚欧亚大陆除外，那里的数据和模型描述了一个大范围的快速速度异常区域，在该区域上，变化似乎是热起源的。在其他地区，例如中美洲和东南非洲，在PcP和ScS中观察到相关的短尺度横向变化（几百公里），这意味着R =（偏导数）ln V_s /（偏导数）的值较大但不会过大）ln V_p（〜2.5）。另一方面，至少在两种情况下，在非洲羽绒的心脏和太平洋羽绒的边缘，P和S速度的变化似乎是不相容的，这意味着在组成不同的区域上存在强烈的横向梯度，这可能还涉及地幔边界上的地形。在从全局数据集中估计地幔底部的R值时应谨慎，因为P和S数据集的不同平滑和采样可能会导致强烈的偏差和毫无意义的结果。

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