Inversion and Geodiversity: Searching Model Space for the Answers

Abstract

Geophysical inversion employs various methods to minimize the misfit between geophysical datasets and three-dimensional petrophysical distributions. Inversion techniques rely on many subjective inputs to provide a solution to a non-unique underdetermined problem, including the use of a priori model elements (i.e. a contiguous volume of the same litho-stratigraphic package), the a priori input model itself or inversion constraints. In some cases, inversion may produce a result that perfectly matches the observed geophysical data, but can still misrepresent the geological system. A workflow is presented here that offers objective methods to provide inputs to inversion: (1) simulations are performed to create a model suite that contains a range of geologically possible models; (2) stratigraphic variability is determined via uncertainty analysis to identify low certainty model regions and elements; (3) geodiversity analysis is then conducted to determine geometrical and geophysical extremes and commonalities within the model space; (4) geodiversity metrics are simultaneously analysed using principal component analysis to identify the contribution of different model elements toward overall model suite uncertainty; (5) principal component analysis also determines which models exhibit diverse or common geological and geophysical characteristics which (6) facilitate the selection of models as inputs to geophysical inversion. This workflow is applied to a three-dimensional model of the Ashanti Greenstone Belt, southwestern Ghana in West Africa in order to reduce the subjectivity incurred during decision making, explore the range of geologically possible models and provide geological constraints to the inversion process to produce geologically and geophysically robust suites of models. Results further suggest that three-dimensional uncertainty grids can optimize inversion processes and assist in finding geologically reasonable solutions.