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On accurate geoid modeling: derivation of dirichlet problems that govern geoidal undulations and geoid modeling by means of the finite difference method and a hybrid method

机译:关于精确的大地水准面建模:通过有限差分方法和混合方法推导控制大地水准面波动的狄利克雷问题和大地水准面模型

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The geoid is the reference surface used to measure heights (orthometric). These are used to study any mass variability in the Earth system. As the Earth is represented by an oblate spheroid (Ellipsoid), the geoid is determined by geoidal undulations (N) which are the separation between these surfaces. N is determined from gravity data by Stokes's Integral. However, this approach takes a Spherical rather than an Ellipsoidal Earth. Here it is derived a Partial Differential Equation (PDE) that governs N over the Earth by means of a Dirichlet problem and show a method to solve it which precludes the need for a Spherical Earth. Moreover, Stokes's Integral solves a boundary value problem defined over the whole Earth. It was found that the Dirichlet problem derived here is defined only over the region where a geoid model is to be computed, which is advantageous for local geoid modeling. Moreover, the method eliminates several of the sources of uncertainty in Stokes's Integral. However, estimates indicate that the errors due to discretization are very large in this new method which calls for its modification. So, here it is also proposed an optimal combination of techniques by means of a Hybrid method and shown that it alleviates the uncertainty in Finite Difference Method. Moreover, a rigorous error analysis indicates that the Hybrid method proposed here may well outperform Stokes's Integral.
机译:大地水准面是用于测量高度(正交)的参考表面。这些用于研究地球系统中的任何质量变异性。由于地球由扁球体(椭球体)表示,因此,大地水准面由大地水准波动(N)决定,大地水准波动是这些表面之间的间隔。 N是由斯托克斯积分从重力数据中确定的。但是,这种方法采用的是球形而不是椭圆形的地球。在此推导了偏微分方程(PDE),该方程通过Dirichlet问题控制了地球上的N,并显示了解决该问题的方法,该方法无需使用球形地球。此外,斯托克斯积分解决了整个地球上定义的边值问题。已经发现,这里导出的狄利克雷问题仅在要计算大地水准面模型的区域上定义,这对于局部大地水准面建模是有利的。此外,该方法消除了斯托克斯积分中不确定性的几种来源。但是,估计表明在这种需要对其进行修改的新方法中,由于离散导致的误差非常大。因此,在此还提出了一种通过混合方法的最佳技术组合,并表明它减轻了有限差分法中的不确定性。此外,严格的误差分析表明,此处提出的混合方法可能会胜过斯托克斯积分。

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