Improving DC Resistivity Inversions in the Athabasca Basin through Geological and Physical Property Constraints

Clint Keller, Senior Geophysicist, Cameco Corporation

Clint graduated from the University of Saskatchewan with a Bachelor of Science Honours degree in geophysics. During his summer breaks in university, he worked as a summer student at Cameco and remains there 15 years later as a Sr. Geophysicist.  

Clint has worked on uranium exploration projects around the world, but the majority of his focus has been on the prolific Athabasca Basin metallogenic province in northern Saskatchewan. Clint specializes in processing, inversion, and interpretation of geophysical data and its integration with other geoscientific data in the search for Cameco’s next deposit which will be used  to energize a clean-air world.

Talk Abstract:

Geophysics plays a key role in the exploration for uranium mineralization in the Athabasca Basin region, particularly when exploring deep under cover. Direct current (DC) resistivity data is commonly used to map hydrothermal clay alteration in the lower Athabasca sandstone cover rocks typically associated with unconformity-related uranium mineralization. 

Since the geology beneath a geophysical survey is never fully understood, questions will always remain when interpreting inversions of real geophysical field data. However, by first evaluating various inversion parameters and the subsequent results on synthetic data, learnings from a known model can then be applied to real field data with greater confidence. By modelling synthetic DC resistivity data and evaluating the impact of various inversion parameters as well as geological and physical property constraints, an optimized inversion methodology can be implemented. The incorporation of geological constraints provides additional information to help steer the inversion to the proper solution space and is shown to significantly improve results. In this study, synthetic data is analyzed and inverted in an attempt to recover models “closer to geological reality”. These learnings are then applied to real field data where results are significantly improved and correlate well with confirmed geological results.

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