TY - JOUR A1 - Ley-Cooper, Alan Yusen A1 - Viezzoli, Andrea A1 - Guillemoteau, Julien A1 - Vignoli, Giulio A1 - Macnae, James A1 - Cox, Leif A1 - Munday, Tim T1 - Airborne electromagnetic modelling options and their consequences in target definition JF - Exploration geophysics : the bulletin of the Australian Society of Exploration Geophysicists N2 - Given the range of geological conditions under which airborne EM surveys are conducted, there is an expectation that the 2D and 3D methods used to extract models that are geologically meaningful would be favoured over ID inversion and transforms. We do after all deal with an Earth that constantly undergoes, faulting, intrusions, and erosive processes that yield a subsurface morphology, which is, for most parts, dissimilar to a horizontal layered earth. We analyse data from a survey collected in the Musgrave province, South Australia. It is of particular interest since it has been used for mineral prospecting and for a regional hydro-geological assessment. The survey comprises abrupt lateral variations, more-subtle lateral continuous sedimentary sequences and filled palaeovalleys. As consequence, we deal with several geophysical targets of contrasting conductivities, varying geometries and at different depths. We invert the observations by using several algorithms characterised by the different dimensionality of the forward operator. Inversion of airborne EM data is known to be an ill-posed problem. We can generate a variety of models that numerically adequately fit the measured data, which makes the solution non-unique. The application of different deterministic inversion codes or transforms to the same dataset can give dissimilar results, as shown in this paper. This ambiguity suggests the choice of processes and algorithms used to interpret AEM data cannot be resolved as a matter of personal choice and preference. The degree to which models generated by a ID algorithm replicate/or not measured data, can be an indicator of the data's dimensionality, which perse does not imply that data that can be fitted with a 1D model cannot be multidimensional. On the other hand, it is crucial that codes that can generate 2D and 3D models do reproduce the measured data in order for them to be considered as a plausible solution. In the absence of ancillary information, it could be argued that the simplest model with the simplest physics might be preferred. KW - airborne KW - electromagnetics KW - exploration KW - inversion KW - target Y1 - 2015 U6 - https://doi.org/10.1071/EG14045 SN - 0812-3985 SN - 1834-7533 VL - 46 IS - 1 SP - 74 EP - 84 PB - CSIRO CY - Clayton ER - TY - JOUR A1 - Esfahani, Reza Dokht Dolatabadi A1 - Gholami, Ali A1 - Ohrnberger, Matthias T1 - An inexact augmented Lagrangian method for nonlinear dispersion-curve inversion using Dix-type global linear approximation JF - Geophysics : a journal of general and applied geophysics N2 - Dispersion-curve inversion of Rayleigh waves to infer subsurface shear-wave velocity is a long-standing problem in seismology. Due to nonlinearity and ill-posedness, sophisticated regularization techniques are required to solve the problem for a stable velocity model. We have formulated the problem as a minimization problem with nonlinear operator constraint and then solve it by using an inexact augmented Lagrangian method, taking advantage of the Haney-Tsai Dix-type relation (a global linear approximation of the nonlinear forward operator). This replaces the original regularized nonlinear problem with iterative minimization of a more tractable regularized linear problem followed by a nonlinear update of the phase velocity (data) in which the update can be performed accurately with any forward modeling engine, for example, the finite-element method. The algorithm allows discretizing the medium with thin layers (for the finite-element method) and thus omitting the layer thicknesses from the unknowns and also allows incorporating arbitrary regularizations to shape the desired velocity model. In this research, we use total variation regularization to retrieve the shear-wave velocity model. We use two synthetic and two real data examples to illustrate the performance of the inversion algorithm with total variation regularization. We find that the method is fast and stable, and it converges to the solution of the original nonlinear problem. KW - surface wave KW - nonlinear KW - inversion KW - modeling KW - finite element Y1 - 2020 U6 - https://doi.org/10.1190/geo2019-0717.1 SN - 0016-8033 SN - 1942-2156 VL - 85 IS - 3 SP - EN77 EP - EN85 PB - GeoScienceWorld CY - Tulsa, Okla. ER -