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Estimation of the gravitational potential energy of the earth based on different density models

机译:基于不同密度模型的地球重力势能估计

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The estimation of the Earth’s gravitational potential energy E was obtained for different density distributions and rests on the expression E = − (Wmin + ΔW) derived from the conventional relationship for E. The first component Wmin expresses minimum amount of the work W and the second component ΔW represents a deviation from Wmin interpreted in terms of Dirichlet’s integral applied on the internal potential. Relationships between the internal potential and E were developed for continuous and piecewise continuous density distributions. The global 3D density model inside an ellipsoid of revolution was chosen as a combined solution of the 3D continuous distribution and the reference PREM radial piecewise continuous profile. All the estimates of E were obtained for the spherical Earth since the estimated (from error propagation rule) accuracy σE of the energy E is at least two orders greater than the ellipsoidal reduction and the contribution of lateral density inhomogeneities of the 3D global density model. The energy E contained in the 2nd degree Stokes coefficients was determined. A good agreement between E = EGauss derived from Gaussian distribution and other E, in particular for E = EPREM based on the PREM piecewise continuous density model and E-estimates derived from simplest Legendre-Laplace, Roche, Bullard and Gauss models separated into core and mantle only, suggests the Gaussian distribution as a basic radial model when information about density jumps is absent or incomplete.
机译:对于不同的密度分布,获得了地球重力势能E的估计值,并基于从E的常规关系得出的表达式E = −(W min +ΔW)。第一个分量W min 表示功W的最小值,第二个分量ΔW表示与W min 的偏差,W min 用施加在内部电势上的Dirichlet积分解释。建立了内部电势与E之间的关系,以实现连续和分段连续的密度分布。选择旋转椭球内部的全局3D密度模型作为3D连续分布和参考PREM径向分段连续轮廓的组合解决方案。所有E的估计值都是针对球形地球得出的,因为能量E的估计值(根据误差传播规则)的准确度σ E 比椭球减小和横向密度的贡献至少大两个数量级。 3D整体密度模型的不均匀性。确定包含在第二度斯托克斯系数中的能量E。高斯分布的E = E Gauss 与其他E之间的良好协议,特别是对于基于PREM分段连续密度模型的E = E PREM 和导出的E估计仅从最简单的勒让德-拉普拉斯(Legendre-Laplace)模型,罗氏(Roche)模型,布拉德(Bullard)模型和高斯(Gauss)模型中分离出来的模型就表明,当缺乏密度跳跃信息或不完全信息时,高斯分布作为基本的径向模型。

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