Publication 167
Magnetotelluric Inversion based on Mutual Information
Eric Mandolesi and Alan G Jones
Abstract
Joint inversion of different geophysical datasets is becoming a more popular and powerful
tool, and it has been performed on data sensitive both to the same physical parameter and
to different physical parameters. Joint inversion is undertaken to reduce acceptable model
space and to increase sensitivity to model parameters that one method alone is unable to
resolve adequately. We examine and implement a novel hybrid joint inversion approach.
In our inversion scheme a model – the reference model – is fixed, and the information
shared with the subsurface structure obtained from another method will be maximized; in
our case conductivity structures from magnetotelluric (MT) inversion. During inversion,
the joint probability distribution of the MT and the specified reference model is estimated
and its entropy minimized in order to guide the inversion result towards a solution that
is statistically compatible with the reference model. The powerful feature of this technique
is that no explicit relationships between estimated model parameters and reference
model ones are presumed: if a link exists in data then it is highlighted in the estimation of
the joint probability distribution, if no link is required, then none is enforced. Tests performed
verify the robustness of this method and the advantages of it in a 1D anisotropic
scenario are demonstrated. A case study was performed with data from Central Germany,
effectively fitting an MT dataset from a single station within as minimal an amount of
anisotropy as required.
Source
Geophysical Journal International, accepted 03 July, 2014. [PDF preprint]
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Alan G Jones / 03 July 2014 /
alan-at-cp.dias.ie