Joint Probabilistic Inversion of 3D Magnetotelluric and Seismic Data in Southeast Australia&#160;
Authors: Manassero Maria Constanza , Afonso Juan Carlos , Kirkby Alison , Jones Alan , Fomin Ilya , Czarnota Karol 
Published: March 2022

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&lt;p&gt;In the context of whole-lithosphere structure, the joint inversion of magnetotelluric (MT) with seismic data is particularly interesting as they provide complementary information on the thermal structure, fluid pathways and water content. Both data sets can put tight constrains on the first-order thermal structure and mineralogical structure of the lithosphere, but only MT is strongly sensitive to anomalous features such as hydrogen content, minor conductive phases and/or small volumes of fluid or melt. This makes joint inversions of MT with other observables a powerful means to detect fluid pathways in the lithosphere including the locus of partial melting, ore deposits and hydrated (or metasomatized) lithologies. This unique potential of joint inversions of MT with other datasets has given impetus to the acquisition of collocated MT and seismic data over large regions. Concrete examples are the US Array, Sinoprobe in China, and the AusLAMP/AusArray in Australia. These multi-disciplinary programs are providing high-quality seismic and MT data with unprecedented resolution and coverage, allowing the pursuit of large-scale 3D joint inversions to image the structure, dynamics and evolution of the whole lithosphere and upper mantle.&lt;/p&gt;
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&lt;p&gt;Within probabilistic approaches the solution to the inverse problem is given by the so-called posterior probability density function which provides complete information about the unknown parameters and their uncertainties conditioned on the data and modelling assumptions. Joint probabilistic inversions of MT and seismic data have been successfully implemented in the context of 1D MT data only. For the cases of 2D and 3D MT data, however, the large computational cost of the MT forward problem has been the main impediment for pursuing probabilistic inversions, as the number of forward solutions required are typically on the order of 10&lt;sup&gt;5&lt;/sup&gt; &amp;#8211; 10&lt;sup&gt;7&lt;/sup&gt;. To overcome this limitation, we have recently presented a novel strategy&amp;#160;[2,3], called RB+MCMC, that computes 3D MT surrogate models and uses complementary parameterizations to couple different data sets. This strategy reduces the computational cost of the 3D MT forward solver and allow us to perform full joint probabilistic inversions of MT and other datasets for the 3D imaging of deep thermochemical anomalies.&lt;/p&gt;
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&lt;p&gt;In this contribution, we first illustrate the benefits and general capabilities of our method for 3D joint probabilistic inversions of MT with other datasets using whole-lithosphere synthetic models. Last, as part of the Exploring for the Future program, we present results of the first joint probabilistic inversion of 3D MT in southeast Australia using the AusLAMP data and a seismic velocity model derived from teleseismic tomography [4]. These results demonstrate the capabilities of our conceptual and numerical framework for 3D joint probabilistic inversions of MT with other geophysical data sets and open up exciting opportunities for elucidating the Earth&amp;#8217;s interior in other regions.&lt;/p&gt;
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&lt;/div&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;[1] Afonso, J.C. et al., (2016), Journal of Geophysical Reseach, 121, doi:10.1002/2016JB013049&lt;/p&gt;&lt;p&gt;[2] Manassero, M. C., et al., (2020), Geophysical Journal International, 223(3), doi: 10.1093/gji/ggaa415&lt;/p&gt;&lt;p&gt;[3] Manassero, M. C., et al., (2021), doi: 10.1029/2021JB021962&lt;/p&gt;&lt;p&gt;[4] Rawlinson, N., et al., (2016), Tectonophysics, doi: 10.1016/j.tecto.2015.11.034&lt;/p&gt;