
%0 Journal Article
%A Lindsay, Mark 
%A Perrouty, Stephane 
%A Ailleres, Laurent 
%A Jessell, Mark 
%T Geological uncertainty and geophysical misfit: How wrong can we be?
%J ASEG Extended Abstracts
%D 2013/08/12
%V 2013
%N 1
%P 1-5
%K 
%R https://doi.org/10.1071/ASEG2013AB322
%U https://www.publish.csiro.au/paper/ASEG2013AB322 
%X Geophysical inversion employs numerical methods to minimise the misfit between three-dimensional petrophysical distributions and geophysical datasets. Inversion techniques rely on many subjective inputs to provide a solution to a non-unique problem, including use of an a priori input model or model elements (a contiguous volume of the same litho-stratigraphic package) and inversion constraints. Inversions may produce a result that perfectly matches the observed geophysical data but still misrepresents the geological system. A workflow is presented that offers objective methods to provide inputs to inversion. First, simulations are performed to create a model suite that contains a range of geologically possible models. Next, uncertainty analysis is performed using stratigraphic variability to identify low certainty model regions and elements. Geodiversity analysis is then conducted to determine the geometrical and geophysical extremes within the model space. Next, geodiversity metrics are then simultaneously analysed using principal component analysis to identify the geometrical and geophysical aspects that contribute most toward model suite variability. Principal component analysis determines which models exhibit common or diverse geological and geophysical characteristics, facilitating selection of models subjected to geophysical inversion. We apply this workflow to the Ashanti Greenstone Belt, southwestern Ghana in west Africa. The workflow described in this manuscript reduces the subjectivity during decision making, explores the range of geologically possible models and provides geological constraints to the inversion process with the aim of producing geologically and geophysically robust suites of models associated with an uncertainty grid. 
