Aeromagnetic data analysis using the 2D directional continuous wavelet transform (DCWT)

Pattern recognition of structural boundaries from aeromagnetic data using the 2-D continuous wavelet transform and the 3-D analytic signal

“…This interest originated in the analogy between the upward continuation and the wavelet transform (Fedi and Cascone, 2008;Ouadfeul et al, 2010). The modulus of the CWT of a potential field F(x, y) with the Poisson kernel as analyzing wavelet is equivalent to the same field upwarded to a level Z = a (Ouadfeul et al, 2012). In this paper, we will analyze the amplitude of the 3-D analytic signal by the 2-D continuous wavelet transform with the cited analyzing wavelet for the purpose of providing a structural boundaries pattern from aeromagnetic data.…”

“…This is due to the inclination and declination effects. Firstly, we have tested the method proposed by Ouadfeul et al (2012) to delineate structural boundaries in cases of high remanence. This last step is based only to the mapping of maxima of the 2-D directional continuous wavelet transform.…”

“…where d j is a real number, N max = 67, and M max = 67. Ouadfeul et al (2012) showed that the boundaries of structures are delineated by mapping maxima of the CWT for each scale; scales are varied following a power law of two from 424 to 11 818.98 m. Maxima of the CWT are mapped in Fig. 2c.…”

“…The wavelet transform has been widely used for potential gravity and magnetic data analysis in the last decade, with the goal to provide a model of causative sources; one use of such a model is to predict a pattern of geological boundaries. Ouadfeul et al (2012) and Ouadfeul and Aliouane (2012a, b) have published papers on the structural boundaries delimitation from gravity and magnetic data using a type of directional continuous wavelet transform introduced by Murenzi (1989). Ouadfeul et al (2012) have demonstrated that the directional continuous wavelet transform requires a reduction to the pole (RTP) for better causative sources characterization and it is very sensitive to noise.…”

“…Ouadfeul et al (2012) and Ouadfeul and Aliouane (2012a, b) have published papers on the structural boundaries delimitation from gravity and magnetic data using a type of directional continuous wavelet transform introduced by Murenzi (1989). Ouadfeul et al (2012) have demonstrated that the directional continuous wavelet transform requires a reduction to the pole (RTP) for better causative sources characterization and it is very sensitive to noise. The RTP requires the knowledge of the magnetic Inclination (I) and Declination (D).…”