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A crustal thickness model of Antarctica calculated in spherical approximation from satellite gravimetric data

Fecha de publicación:
2019, July
Unidad:
Instituto de Astronomia y Ciencias Planetarias de Atacama, Universidad de Atacama
Datos de publicación:
Geophysical Journal International, Volume 218, Issue 1, July 2019, Pages 388–400
Índice de citas:
Science Citation Index Expanded
Enlace:
https://doi.org/10.1093/gji/ggz154
Palabras Claves:
Base de datos:
WoS-Scopus
Descripción:
The ice cap covering Antarctica has long limited our understanding of the continental-scale crustal model due to its inaccessibility and the resulting logistical difficulties when executing geophysical field work, such as seismograph deployment. Resolving a high spatial resolution crustal model for Antarctica where seismographs are sparsely distributed stimulates scientific interest in this relatively less studied continent. In this study, we utilize satellite gravity observations from the global gravity model EIGEN-6C4 to create an alternative crustal thickness model of Antarctica. The gravity data were corrected for sediments, topography and ice cover. Furthermore, we considered the gravity effect due to vertical deformation of the lithosphere caused by ice load besides the earth's curvature in the modelling. We inverted the corrected gravity data using the regularized Bott's inversion method in spherical approximation and constrained the results by seismic observations. This crustal thickness model shows a thicker average crust in East Antarctica and a thinner one in West Antarctica. The thickest crust is in the Gamburtsev Subglacial Mountains with a Moho depth of over 40 km. The thicker crust is particularly evident along the Transantarctic Mountains and the Dronning Maud lands. Comparisons with existing models show a good correlation in gravity-constrained areas. Differences appear in the sedimentary basins and crust with thickness closer to seismic point observations. Overall, our crustal model is relatively improved than the existing gravity derived models.

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