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A New Approach to Incorporate Prior Information in MGS Inversion of ERT/IP Data

The current paradigm in geophysical inversions is to compute the simplest solution according to Occam’s principle. 
The implicit assumption is that the parameters of interest have a smooth spatial distribution, which is rarely 
geologically plausible. An alternative is the Minimum Gradient Support (MGS), a functional that allows to compute 
a regularized inversion favoring sharp contrasts. Its use is currently mostly restricted to research studies and the 
MGS solution is highly sensitive to the selection of a variable called the focusing parameter β. There is still no 
consensus on its optimal choice. To propose a methodology for applying this functional to real applications, the 
MGS is first used on ERT/IP synthetic data mimicking a real case study. For complex geometries, a smooth solution 
is first computed and used as a starting model for sharp MGS inversions. Including prior information on resistivity 
from drillings further improves chargeability features. The developed methodology is then applied to ERT/IP data 
collected on a gold deposit. The method allows different interpretations about the mineralization key properties, 
and seems more indicated to compute plausible electrical resistivity spatial distributions regarding the extensive 
prior geological knowledge. The choice of β is challenging and should be automated in future developments.