Abstract
We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterization of the cover-basement interface. It consists of a cooperative inversion workflow relying on standalone inversion codes. Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining such structural constraints with petrophysical prior information. First, we perform the 1D probabilistic inversion of MT data for all sites and recover the respective probabilities of observing the cover-basement interface, which we interpolate to the rest of the study area. We then calculate the probabilities of observing the different rock units and partition the model into domains defined by combinations of rock units with non-zero probabilities. Third, we combine such domains with petrophysical information to apply spatially-varying, disjoint interval bound constraints to least-squares magnetic data inversion. We demonstrate the proof-of-concept using a realistic synthetic model reproducing features from the Mansfield area (Victoria, Australia) using a series of uncertainty indicators. We then apply the workflow to field data from the prospective mining region of Cloncurry (Queensland, Australia). Results indicate that our integration methodology efficiently leverages the complementarity between separate MT and magnetic data modelling approaches and can improve our capability to image the cover-basement interface. In the field application case, our findings also suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement.
Highlights
Test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterization of the cover-basement interface
Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining such structural constraints with petrophysical prior information
In the field application case, our findings suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement
Summary
35 Geophysical integration has been gaining traction in recent years, be it from the joint or cooperative point of views. A number of approaches for the joint modelling have been developed with the goal of exploiting the complementarities between different datasets (see for instance the reviews of Lelièvre and Farquharson, 2016, and Moorkamp et al, 2016, and references therein). Structural approaches allow to jointly invert datasets with differing sensitivities to the properties of the 45 subsurface through the premise that geology is such that spatial changes in inverted properties should be collocated. Whereas structural and petrophysical approaches are well suited to exploit complementarities between datasets in a quantitative manner, running joint inversion might be, in practice, challenging and requires significantly more computing power than the separate inversions
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