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Practical Tips for 3D Regional Gravity Inversion

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To solve the inverse gravimetric problem, i.e., to estimate the mass density distribution that generates a certain gravitational field, at local or regional scale, several parameters have to be defined such as the dimension of the 3D region to be considered for the inversion, its spatial resolution, the size of its border, etc. Determining the ideal setting for these parameters is in general difficult: theoretical solutions are usually not possible, while empirical ones strongly depend on the specific target of the inversion and on the experience of the user performing the computation. The aim of the present work is to discuss empirical strategies to set these parameters in such a way to avoid distortions and errors within the inversion. In particular, the discussion is focused on the choice of the volume of the model to be inverted, the size of its boundary, its spatial resolution, and the spatial resolution of the a-priori information to be used within the data reduction. The magnitude of the possible effects due to a wrong choice of the above parameters is also discussed by means of numerical examples.
Title: Practical Tips for 3D Regional Gravity Inversion
Description:
To solve the inverse gravimetric problem, i.
e.
, to estimate the mass density distribution that generates a certain gravitational field, at local or regional scale, several parameters have to be defined such as the dimension of the 3D region to be considered for the inversion, its spatial resolution, the size of its border, etc.
Determining the ideal setting for these parameters is in general difficult: theoretical solutions are usually not possible, while empirical ones strongly depend on the specific target of the inversion and on the experience of the user performing the computation.
The aim of the present work is to discuss empirical strategies to set these parameters in such a way to avoid distortions and errors within the inversion.
In particular, the discussion is focused on the choice of the volume of the model to be inverted, the size of its boundary, its spatial resolution, and the spatial resolution of the a-priori information to be used within the data reduction.
The magnitude of the possible effects due to a wrong choice of the above parameters is also discussed by means of numerical examples.

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