
%0 Journal Article
%A Giraud, Jeremie 
%A Pakyuz-Charrier, Evren 
%A Ogarko, Vitaliy 
%A Jessell, Mark 
%A Lindsay, Mark 
%A Martin, Roland 
%T Impact of uncertain geology in constrained geophysical inversion
%J ASEG Extended Abstracts
%D 2018/02/26
%V 2018
%N 1
%P 1-6
%K 
%R https://doi.org/10.1071/ASEG2018ABM1_2F
%U https://www.publish.csiro.au/paper/ASEG2018ABM1_2F 
%X The integration of geological modelling, petrophysics and geophysics in a single inversion scheme is a complex and powerful strategy for solving challenges faced in geoscientific resource exploration. Probabilistic geological modelling and geophysical inversion are non-linear processes that show various degrees of sensitivity to uncertainty in input measurements. Using field geological measurements from the Mansfield area (Victoria, Australia) and synthetic geophysical data, we present a synthetic case study investigating the impact of geological uncertainty on constrained joint geophysical inversion. We investigate the influence of uncertain geological measurements on geologically constrained inversion through a sensitivity analysis to uncertainty in orientation data. Probabilistic geological models used to define constraints for geophysical joint inversion are obtained through a Monte-Carlo based uncertainty estimation (MCUE) method. We simulate a broad range of possible cases through a parameter sweep on uncertainty levels in geological measurements to provide a reference for practitioners. The analysis and comparison of the results at varying uncertainty levels show that results can be grouped into two main categories. For the highest uncertainty levels, significant portions of the models retain the characteristic features of geologically unconstrained inversions. Meanwhile, below a threshold in uncertainty level, inversion benefits from the interaction of geophysical data and geologically conditioned constraints. In such cases, inverted models are improved compared to both the geological modelling alone and geologically unconstrained inversion. The conclusion of this work is that knowledge of this threshold is critical for the interpretation of results and decision making because it indicates whether the datasets provide enough information to take advantage of the complementarities between geological modelling and geophysical inversion. Knowledge of this threshold can also support decision making pertaining to inversion strategies and geological field data collection. 
