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3d Resistivity Inversion Software Downloads =LINK=

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.

3d Resistivity Inversion Software Downloads

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. More often than not, however, the geological structures are complex, and even if careful consideration of electromagnetic survey layouts is taken, e.g. measurements taken perpendicular to the strike direction, the data will inevitably be 3D in places. With the advance in 3D inversion codes and more readily available high-performance computing facilities, magnetotelluric (MT) surveys are now commonly collected in arrays rather than transects and thus need to be inverted using a 3D code. Additionally, where transects are collected, it is now becoming more commonplace to invert using a 3D inversion code to allow for three-dimensionality of data (e.g. Robertson et al. (2016); Meqbel et al. (2016)).

This becomes critical when the models are used for quantitative geological interpretations such as calculating melt or fluid percentage, or determining the cause of a conductor (sulphides, graphite, hydrogen, etc.). To ensure interpretations are accurate, the range of resistivity values that could apply to a given feature should be considered in the interpretation, and an exploration of the model space is needed to determine these ranges. Various model resolution and sensitivity testing has occurred in 3D magnetotelluric inversions (see Miensopust 2017, for a comprehensive summary of these). When a preferred model is chosen, specific features are generally tested in a number of ways such as removing the feature in question and letting the inversion run for a few more iterations to see whether it returns or locking cells to certain values and seeing whether there is an effect on the model fit, etc. (e.g. Kelbert et al. 2012; Yang et al. 2015). Before one settles on a preferred model, there are a lot of parameters that can be tested, e.g. varying the model smoothing parameters, the starting resistivity, the inclusion of a priori information, the components inverted (e.g. full impedance tensor or off-diagonals only, the tipper, the phase tensor, etc.), the cell size, the method of error calculation and the error floors or the interstation sampling rate (e.g. inverting every second site). Testing all of these parameters is time-consuming and impractical in cases where significant computational time is not available, or model results are required quickly.

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). AusLAMP is the Australian Lithospheric Architecture Magnetotelluric Project, which aims to provide a 3D image of the electrical resistivity distribution of the crust and mantle beneath the Australian continent by acquiring long-period MT data at approximately 2800 sites across Australia at half-degree intervals (approximately 55 km).

We test the hypothesis that the incorporation of known resistivities into the prior model returns a better model than using a half-space and/or may reduce the number of iterations required. By known, we mean information that is reasonably and consistently inferred from other geophysical datasets. To do this, we used a half-space (no ocean) with resistivity of 100 \(\Omega \)m and covariance of 0.4 to compare to models with one individual known feature added at a time. The known features are as follows: bathymetry (with ocean resistivity of 0.3 \(\Omega \)m), 10 \(\Omega \)m resistivity beneath the 410 km seismic discontinuity and 1 \(\Omega \)m resistivity beneath the 660 km seismic discontinuity. The inclusion of these features is described in more detail in the following sections, and the inversions are summarised in Table 1 and plotted in Figs. 4 and 5.

It is commonplace in 3D magnetotelluric inversion to include bathymetry in the prior model. Sea water has a very low resistivity of about 0.3 \(\Omega \)m and thus generally is in stark contrast with the much more resistive lithosphere. The existence of sea water around the survey area also severely affects observed MT responses, known as the geomagnetic coast effect (Parkinson and Jones 1979). The sea has a substantial influence on observed MT data particularly when the separation distance from the coast is smaller than the skin depth of the frequency of interest. The skin depth of a typical long-period MT survey (such as this study) can readily reach up to a few hundred kilometres, so ocean effects can be noticeable over quite some distance. However, the closest point of the survey area to the ocean is about 200 km and the inclusion of bathymetry had little effect on the resultant inversion with the RMS remaining similar (9% increase from 1.82 to 1.98), and the inversions visually are almost identical. The inversion converged with 10 less iterations than the half-space starting model inversion.

ModEM3DMT allows the user to input a starting model and a prior model. The prior model is a compulsory input, and by default if no starting model is given, then the model starts from the prior model. In regions of the model poorly constrained by the data (e.g. in areas outside of the survey area or at depths exceeding MT signal penetration), the resistivities usually revert to the resistivity of the prior model. No independent starting model was included in any of the tests; thus, the start model is identical to the prior model. A variety of resistivities were tested for the prior/starting model (half-space plus ocean where ocean is locked at resistivity of 0.3 \(\Omega \)m, with bathymetry taken from ). The land part of the model was varied to resistivity values that were evenly spaced on a log scale, 10, 31.6, 100, 316 and 1000 \(\Omega \)m (1, 1.5, 2, 2.5 and 3 on log scale). In addition, the average apparent resistivity of all data points across all sites and all periods was calculated to be 69 \(\Omega \)m, and this was used also as a prior model. Depth slices (Fig. 9) and cross sections (Fig. 10) show key features of the models.

The RMS, KSU (divided by ten for plotting purposes) and the RMS variance (left vertical axis) and the starting RMS (right vertical axis) for models with starting resistivity for seven different inversions with differing starting half-spaces, 3, 10, 31, 69, 100, 316 and 1000 \(\Omega \)m

The averaged resistivity values taken from the inversions with different resistivities for the prior model ranging from 3 to 1000 \(\Omega \)m, for depths of 10 to 140 km (left) and 10 to 450 km (right)

The averaged resistivity of the most conductive model (3 \(\Omega \)m prior) and the most resistive model (1000 \(\Omega \)m prior) is most similar in lower crustal to shallow upper mantle depths (Moho depth \(\sim \) 36 km in most of the model region; Kennett et al. 2011) where sensitivity peaks. The smallest spread occurs around 40 km depth where the resistivity varies by 0.733log \(\Omega \)m between these models (from 3.19log \(\Omega \)m for the 1000 \(\Omega \)m prior to 2.46log \(\Omega \)m for the 3 \(\Omega \)m prior). However, if we restrict the analysis by eliminating the 316 and 1000 \(\Omega \)m (due to a significantly poorer fit of these models), the range decreases to just 0.06log \(\Omega \)m (from 2.52log \(\Omega \)m for the 100 \(\Omega \)m prior to 2.46log \(\Omega \)m for the 3 \(\Omega \)m prior). At 10 km depth, the range (again excluding 316 and 1000 \(\Omega \)m) is 0.12log \(\Omega \)m (from 2.45log \(\Omega \)m for the 100 \(\Omega \)m prior to 2.33log \(\Omega \)m for the 3 \(\Omega \)m prior), and by 100 km, the range is 0.92log \(\Omega \)m (from 2.59log \(\Omega \)m for the 100 \(\Omega \)m prior to 1.67log \(\Omega \)m for the 3 \(\Omega \)m prior). These results highlight the importance of choosing a reasonable prior model as the absolute values of the resistivity of the converged model is very dependent on the starting model. Whilst these results do not give a definitive answer of which of these models is best, we have confidence that 316 and 1000 \(\Omega \)m are too resistive as indicated by the substantially higher initial and final RMS values (Figs. 6 and 7). Averaging the apparent resistivity for every site and every period as we did with the 69 \(\Omega \)m model similar to the method of Meqbel et al. (2014) seems like a suitable approach for a ball-park resistivity for a prior model half-space with the RMS of 1.84 for this model 10% higher than the best achieved RMS of 1.66. In regions where sedimentation is known to occur, a lower starting resistivity (using the averaged resistivity across only the shortest periods) may serve to minimise the 'speckling' in shallow inversion slices; however, we note that in our tests these low starting resistivities introduce large heterogeneities in deep model slices which should be treated with caution (e.g. 172 km depth slice for 3 and 10 \(\Omega \)m models in Fig. 9).

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