Gravity‐Derived Antarctic Crustal Thickness Based on the Gauss‐FFT Method

Abstract

The information on crustal thickness in Antarctica can provide significant constraints on its crustal deformation and tectonic evolution. To generate reliable images of crustal features, we investigate the model of Moho depth and crustal thickness beneath Antarctica by applying the Bott‐Parker's formulas based on the Gauss‐fast Fourier transform method through a comprehensive analysis of gravity data, ice and sediment thicknesses and bedrock elevation, combined with seismic constraints. Tests with synthetic data indicate that the iterative inversion algorithm can yield a highly accurate Moho topography. Ultimately, inverted crustal thickness reveals a more detailed crustal image by clearly identifying more tectonic elements at different scales than previous results and correlates well with major tectonic provinces. Airy isostasy and flexural isostasy models are used to assess the crustal isostatic compensation. The distinctive negative isostatic anomalies are observed in the Transantarctic Mountains and the East Antarctic areas of the great escarpment in the Dronning Maud Land and Aurora and Wilkes Subglacial Basin, indicating that the low density of the uppermost mantle and lithospheric strength may play important roles in compensating for their elevations. Variations in crustal thickness in interior East Antarctica are analyzed; the thickened crust from Dronning Maud Land to the Gamburtsev Subglacial Mountains may be associated with the collision of continental blocks and is interpreted as the fossil sutures. We compare the relationship between the Moho and Curie interfaces and find that the uppermost mantle is magnetized in some areas of East Antarctica, which may indicate preserved Precambrian cratonic roots.

3D density imaging using gravity and gravity gradient in the wavenumber domain and its application in the Decorah

“…This effectively increases computational efficiency. Previous studies have been conducted for many models, and wavenumber domain gravity normalization is a better-developed technical method 12 – 14 . While it is not dependent on a priori models, the wavenumber domain density imaging method exploits the advantages of wavenumber domain orthogonal computation.…”