The discrete wavelet transform (DWT) provides an effective and efficient al
ternative to traditional Fourier and spatial-convolution processing techniq
ues in the enhancement of aeromagnetic data. Standard operators such as hor
izontal and vertical derivatives, integrals of any order, and the Hilbert t
ransform can be diagonalized in the wavelet domain, leading to an efficient
algorithm. The DWT preserves the spatial localization of the components of
the signal, allowing for intelligent discrimination between noise and sign
al in a given frequency range. This, for example, allows for more accurate
calculation of higher order derivatives from noisy signals than is possible
with conventional techniques. Additional accuracy can be gained by using a
cycle-spinning algorithm to minimize local artifacts from the DWT denoisin
g procedure.