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In this paper we tackle the problem of inverting geophysical magnetic data due to simple shape anomalies caused by thin sheet, cylinder and fault models using Occam's inversion scheme. A significant aspect of using Occam's inversion is the choice of the regularization parameter controlling the trade-off between the data fidelity and regularization term in the cost function of optimization problem, and consequently, reliable estimation of subsurface models. Two criteria L-curve and weighted generalized cross validation are considered in order to choose an optimum value of the regularization parameter. The proposed strategy was first evaluated on three theoretical synthetic models for each of the magnetic simple-shaped structures with different random errors, where a considerable agreement was obtained between the exactly known and estimated models. The validity of the technique was also applied to one real data set from Morvarid ironapatite deposit, in Northwest Iran. The resulting inverted parameters using the proposed algorithm correspond reasonably closely with the known geology and nearby borehole information.

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A B S T R A C TPresence of noise in the acquisition of surface nuclear magnetic resonance data is inevitable. There are various types of noise, including Gaussian noise, spiky events, and harmonic noise that affect the signal quality of surface nuclear magnetic resonance measurements. In this paper, we describe an application of a two-step noise suppression approach based on a non-linear adaptive decomposition technique called complete ensemble empirical mode decomposition in conjunction with a statistical optimization process for enhancing the signal-to-noise ratio of the surface nuclear magnetic resonance signal. The filtering procedure starts with applying the complete ensemble empirical mode decomposition method to decompose the noisy surface nuclear magnetic resonance signal into a finite number of intrinsic mode functions. Afterwards, a threshold region based on de-trended fluctuation analysis is defined to identify the noisy intrinsic mode functions, and then the no-noise intrinsic mode functions are used to recover the partially de-noised signal. In the second stage, we applied a statistical method based on the variance criterion to the signal obtained from the initial phase to mitigate the remaining noise. To demonstrate the functionality of the proposed strategy, the method was evaluated on an added-noise synthetic surface nuclear magnetic resonance signal and on field data. The results show that the proposed procedure allows us to improve the signal-to-noise ratio significantly and, consequently, extract the signal parameters (i.e., T * 2 and V 0 ) from noisy surface nuclear magnetic resonance data efficiently.

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