Gravity inversion for rifted margin deep structure using extension and isostatic constraints

Details Gravity inversion for rifted margin deep structure using extension and isostatic constraints Gravity inversion for rifted margin deep structure using extension and isostatic constraints

Aug 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999geoji.138..435c&link_type=abstract

Geophysical Journal International, Volume 138, Issue 2, pp. 435-446.

Astronomy and Astrophysics

Astronomy

Extension, Gravity, Inversion, Isostasy, Rifted Margin

Scientific paper

We present a gravity inversion method for determining the deep structure of rifted margins. It uses a 2-D earth model parametrized as multiple, irregularly shaped, polygonal bodies, each of uniform density. The method has three novel features. First, it links parameters in the shallow parts of the model to those in the deep parts by using a uniform extension model as a constraint in the inversion. The shallow structure will typically be known with greater certainty than the deep structure from shallow seismic and borehole data. Second, it provides for variable weighting of prior information on densities, shapes, the extension model and smoothing to find geologically reasonable models. Third, it estimates densities and shapes simultaneously. The first two features are used to compensate for the inherent deficiencies of poor depth resolution and non-uniqueness in gravity modelling. The last two make the method an efficient way to explore a range of models. Synthetic tests of sensitivity to noise indicate that the isostatic extension constraint promotes the recovery of the short-wavelength Moho topography and eliminates spatial undulations in deep structure due to noise in the data. Synthetic tests of sensitivity to untrue prior information show that the isostatic extension constraint increases the range of acceptable recovered models over no isostatic extension constraint. The range of recovered Moho positions suggests a vertical resolution of about 2 km. Although many recovered models fit the data, the results imply a methodology for choosing a best set of models, and we suggest guidelines for applying the method to real margins.

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