Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure

Regularization methods improve the stability of ill-posed inverse problems by introducing some a priori characteristics for the solution such as smoothness or sharpness. In this contribution, we propose a multidimensional scale-dependent wavelet-based l(1)-regularization term to cure the ill-posedness of the airborne (time-domain) electromagnetic induction inverse problem. The regularization term is flexible, as it can recover blocky, smooth and tunable in-between inversion models, based on a suitable wavelet basis function. For each orientation, a different wavelet basis function can be used, introducing an additional relative regularization parameter. We propose a calibration method to determine (an educated initial guess for) this relative regularization parameter, which reduces the need to optimize for this parameter and, consequently, the overall computation time is under control. We apply our novel scheme to a time-domain airborne electromagnetic data set in Belgian saltwater intrusion context, but the scheme could equally apply to any other 2D or 3D geophysical inverse problem.

Automated summary

Regularization methods improve the stability of ill-posed inverse problems by introducing prior characteristics for the solution. In this paper, a multidimensional scale-dependent wavelet-based l(1)-regularization term is proposed to solve the ill-posed airborne electromagnetic induction inverse problem. The regularization term is flexible and can recover various inversion models based on a suitable wavelet basis function.