大地测量观测和相对海平面联合约束的冰川均衡调整模型

Glacial isostatic adjustment model constrained by geodetic measurements and relative sea level

利用大地测量和历史相对海平面变化数据,结合地震剪切波层析模型,联合确定了新的末次冰期冰川均衡调整(GIA)模型,其中地慢黏滞度不仅沿径向而且沿横向变化.研究思路是,先尝试性地选择比例系数β,利用与地震剪切波速异常的线性关系,计算地幔黏滞度横向扰动,并与横向均匀的黏滞度参考模型叠加给出3D地慢黏滞度模型;再利用耦合拉普拉斯方程的有限元法算法进行GIA预测;然后,重复该过程,直到预测与观测之间的吻合满意为止.主要结论有:(1)给出了横向非均匀的地幔黏滞度模型(RF3L20(β=0.4)),发现了黏滞度显著的横向非均匀性和其对GIA预测的显著影响,指出横向非均匀不完全是由热效应引起的,可能还与化学组分等其他因素有关,该模型可用于地慢动力学研究.(2)给出了全球现今多种GIA预测速率,可为板块运动、陆地水储量、海水质量变化和冰川冰雪质量非平衡监测提供重要的改正.

The new model of the last glacial isostatic adjustment （GIA） is constrained simutaneously by the data sets from geodetic measurements, relative sea levels and seismic shear tomographic data. The viscosities in mantle for this model are allowed to vary both radially andlaterally. Firstly, the scalling factor β is tentatively chosen for which the lateral perturbations inviscosity are computed based on the linear relation with seismic shear wave anomalies. These lateral perturbations in viscosity are then supperposed logarithmically on a laterally homogeneousreference viscosity model to give the total viscosities in the mantle layers. Based on this 3-D viscosity model, the GIA predictions are computed using the coupled-Laplace Finite-Element Method. Such processes will not end untill the predictions agree well with the observed data sets. Themain results determined predictions are as follows. Firstly, th from which the obvious are found. It is indicated e laterally heterogeneous viscosity model （RF3L20（ β =0.4）） is lateral heterogeneities and the prominouced effects on GIA the lateral heterogeneities are attributable not just to themaleffects but also to lateral variations in chemistry and other factors. The RF3L20（ β =0.4） model can be used to the modelling in mantle geodynamics. Secondly, the multiple present GIA ratesare predicted that can be served as the important corrections for the monitoring of the tectonic motion, land water storage changes, ocean water mass variations and the non-equilibrium of the ice and snow.