Abstract
3D inversions of magnetotelluric data are now almost standard, with computational power now allowing an inversion to be performed in a matter of days (or hours) rather than weeks. However, when compared to 2D inversions, these are still very computationally demanding. As a result, 3D inversions are generally not subjected to as rigorous testing as a 1D or 2D inversion would be, which has implications when these models are used for geological interpretation. In this study, we explore the parameter space for inversion of continent-scale datasets. The generalisations made regarding the effects of each parameter should also be scalable to smaller surveys and will enable MT practitioners to optimise their results. We have performed testing on a subset of the South Australian component of the eventual Australia-wide AusLAMP (Australian Lithospheric Architecture Magnetotelluric Project). The subset was inverted with different parameters, model setup and data subsets. Specifically, results from testing of the model covariance, the resistivity of the prior model, the inclusion of 'known' information into the prior model, the model cell size, the data components inverted for and the damping parameter lambda were all investigated. In our testing of the 3D inversion software, ModEM3DMT, we found that the resistivity of the starting/prior model had significant effect on the final model. Careful selection of initial lambda value can aid in reducing computational time whilst having a negligible effect on the resultant model, whilst large covariance values and model cell sizes enhanced conductive features at depth.
Highlights
The electrical resistivity structure of Earth is 3D
We present recommendations for modelling MT arrays using the inversion code ModEM3DMT (Egbert and Kelbert 2012; Kelbert et al 2014), tested by performing many 3D inversions on AusLAMP data in northeast South Australia (Fig. 1)
The inversions were run on Raijin, a high-performance computational facility of the National Computational Infrastructure (NCI), in parallel across 48 cores. (Optimal number of cores is equal to twice the number of periods inverted + 1.) The model parameters that are generally unchanged whilst other parameters were investigated are as follows
Summary
The electrical resistivity structure of Earth is 3D. In a sedimentary basin, it often approximates to 1D. If geological structures have consistent strike direction across a region such as a long fault plane, maybe it approximates to 2D resistivity structure. Ensembles of models from stochastic inversion methods will provide a variety of solutions that fall within the acceptable array of model parameters (Muñoz and Rath 2006). This is available in 1D (e.g. Cerv et al 2007), or in 2D (e.g. Chen et al 2012), but these types of probabilistic methods are difficult to realise in three dimensions due to the very expensive computational nature of this process when performing
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