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LoadDef:一个基于Python的工具包,用于模拟球对称物体表面质量负荷引起的弹性变形。

LoadDef: A Python-Based Toolkit to Model Elastic Deformation Caused by Surface Mass Loading on Spherically Symmetric Bodies.

作者信息

Martens Hilary R, Rivera Luis, Simons Mark

机构信息

Department of Geosciences University of Montana Missoula MT USA.

Institut de Physique du Globe de Strasbourg, UMR 7516 CNRS, Université de Strasbourg Strasbourg France.

出版信息

Earth Space Sci. 2019 Feb;6(2):311-323. doi: 10.1029/2018EA000462. Epub 2019 Feb 6.

DOI:10.1029/2018EA000462
PMID:31008150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6472329/
Abstract

Temporal variations of surface masses, such as the hydrosphere and atmosphere of the Earth, load the surfaces of planetary bodies causing temporal variations in deformation. Surface shear forces and gravitational fields also drive ongoing planetary deformation. Characterizing the spatiotemporal patterns of planetary deformation can constrain allowable models for the interior structure of a planetary body as well as for the distribution of surface and body forces. Pertinent applications include hydrology, glaciology, geodynamics, atmospheric science, and climatology. To address the diversity of emerging applications, we introduce a software suite called LoadDef that provides a collection of modular functions for modeling planetary deformation within a self-consistent, Python-based computational framework. Key features of LoadDef include computation of real-valued potential, load, and shear Love numbers for self-gravitating and spherically symmetric planetary models; computation of Love-number partial derivatives with respect to planetary density and elastic structure; computation of displacement, gravity, tilt, and strain load Green's functions; and computation of three-component surface displacements induced by surface mass loading. At a most basic level, only a planetary-structure model and a mass-load model must be supplied as input to LoadDef to utilize all the main features of the software. The end-to-end forward-modeling capabilities for mass-loading applications lay the foundation for sensitivity studies and geodetic tomography. LoadDef results have been validated with Global Navigation Satellite System observations and verified against independent software and published results. As a case study, we use LoadDef to predict the solid Earth's elastic response to ocean tidal loading across the western United States.

摘要

诸如地球水圈和大气圈等地表质量的时间变化,会对行星体表面产生负荷,从而导致变形的时间变化。表面剪切力和引力场也会驱动行星持续变形。表征行星变形的时空模式可以限制行星体内部结构以及表面力和体力分布的可行模型。相关应用包括水文学、冰川学、地球动力学、大气科学和气候学。为了应对新出现的各种应用,我们引入了一个名为LoadDef的软件套件,它提供了一组模块化函数,用于在基于Python的自洽计算框架内对行星变形进行建模。LoadDef的关键特性包括为自引力和球对称行星模型计算实值势、负荷和剪切勒夫数;计算勒夫数关于行星密度和弹性结构的偏导数;计算位移、重力、倾斜和应变负荷格林函数;以及计算由表面质量负荷引起的三分量表面位移。在最基本的层面上,只需提供一个行星结构模型和一个质量负荷模型作为LoadDef的输入,即可利用该软件的所有主要特性。质量负荷应用的端到端正向建模能力为敏感性研究和大地测量层析成像奠定了基础。LoadDef的结果已通过全球导航卫星系统观测进行了验证,并与独立软件和已发表的结果进行了比对。作为一个案例研究,我们使用LoadDef来预测美国西部固体地球对海洋潮汐负荷的弹性响应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/ba3547d38891/ESS2-6-311-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/943a43f18bd1/ESS2-6-311-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/bccf6398e357/ESS2-6-311-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/cfbb45248113/ESS2-6-311-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/7d8938b03ef5/ESS2-6-311-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/ba3547d38891/ESS2-6-311-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/943a43f18bd1/ESS2-6-311-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/bccf6398e357/ESS2-6-311-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/cfbb45248113/ESS2-6-311-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/7d8938b03ef5/ESS2-6-311-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bdf/6472329/ba3547d38891/ESS2-6-311-g005.jpg

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引用本文的文献

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本文引用的文献

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Tracking the weight of Hurricane Harvey's stormwater using GPS data.利用GPS数据追踪哈维飓风雨水的重量。
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Tidal tomography constrains Earth's deep-mantle buoyancy.潮汐层析成像约束了地球深部地幔的浮力。
Nature. 2017 Nov 15;551(7680):321-326. doi: 10.1038/nature24452.
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