Along-track nonlinear filtering of airborne gravity data from the GRAV-D campaign: case study for the ‘Colorado geoid experiment’

The study presents along-track nonlinear diffusion filtering of the airborne gravity data. At first, the provided airborne gravity disturbances from the GRAV-D campaign are transformed into the airborne complete Bouguer disturbances (CBD). This aims to reduce a correlation of the filtered data with the topography. Then the nonlinear diffusion filtering in 1D based on the Perona-Malik model is applied. In this model, a diffusivity coefficient depends on the edge detector, which allows reducing noise while preserving important gradients in the filtered data. As a numerical method we use the finite volume method (FVM). The derived semi-implicit numerical scheme leads to a three-diagonal system matrix that is solved in every iterative step. Here the diffusivity coefficients are updated in every step by new values of the edge detector recomputed from the previous solution.

The numerical experiment presents the along-track nonlinear filtering of the airborne CBD in high mountainous area of the ‘Colorado geoid experiment’. Afterwards, the along-track filtered data are gridded into a 2D map of the airborne CBD. The obtained results show that an appropriate choice of a sensitivity parameter of the diffusivity coefficient can better detect significant structures in the airborne CBD, especially their edges that are usually smoothed by the Gaussian filtering. Finally, the filtered and gridded airborne CBD are backward transformed into the airborne gravity disturbances.