Geophysical inverse problems try to infer the value of a physical property of the earth from data measured at the boundary of the domain. Model parameterization is a key concept to make the inverse problem less ill conditioned. In this contribution we compare the performance of four different basis functions, Fourier, Pixel, Haar and Daubechies Wavelets, for a 1D linear continuous inverse problem. Adopting the right parameterization also reduces the number of dimensions in which the inverse problem is going to be solved, allowing easy posterior analysis. To compare the different basis expansions we have studied a simple 1D linear inverse problem in gravimetric inversion for a density anomaly with Gaussian shape. For this simple toy-problem we show that the Fourier basis gives a better reconstruction using Filon's quadrature to avoid numerical instabilities caused by highly oscillating functions, both, in the noise-free and noisy cases. Besides, the Fourier base is the one that provides the lowest reconstruction error for the number of basis terms and the highest condition number. The Haar and pixel (piecewise-continuous functions) bases provide similar results, although the pixel base needs more terms to achieve lower reconstruction errors. Also, the pixel basis is the one that has the lowest condition number that is obviously related to the way the energy in the system matrix is distributed. Besides, the Daubechies Db2 basis expansion is the one that has the highest system rank, it is more difficult to apply to project the integral kernel, and provides the worst results, compared to the other basis expansions. Finally we propose how to generalize this methodology to linear inverse problems in several dimensions and to non-linear problems. A separate paper will be devoted to this subject.
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