ABSTRACT
This chapter considers processing the inversion's solution for the derivative and integral attributes of the data that satisfy analysis objectives. These objectives commonly focus on isolating and enhancing data features either by computationally laborious convolution in the data domain or numerically simpler filtering in the frequency (i.e., spectral) domain. The convolution theorem relates data domain convolution to frequency domain filtering and vice versa. Hence, using the Fourier transform to maintain the analysis in the domain where filtering holds optimizes computational efficiency. Both data and frequency domain techniques are available to address data isolation and enhancement objectives. Data domain methods include quantitative modeling of known environmental features, graphical smoothing of profiles or contours, computing trend surfaces, and developing convolution grid operators for data smoothing, differentiation, integration, wavelength separation, interpolation, continuation to different altitudes, etc. In modern data analysis, however, the analytical grid methods have been largely superseded by more efficient spectral filtering techniques.
