Quality over quantity: on workflow and model space exploration of 3D inversion of MT data

<jats:title>Abstract</jats:title><jats:p>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 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\lambda $$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math></jats:alternatives></jats:inline-formula> 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 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\lambda $$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math></jats:alternatives></jats:inline-formula> 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.</jats:p>