Novel approach to joint 3D inversion of EM and potential field data using Gramian constraints
One of the major challenges in interpretation of geophysical data remains the ability to jointly invert multiple geophysical datasets for self-consistent 3D earth models of different physical properties. To date, various attempts at 3D joint inversion have been based on either correlations between different physical properties, or by introducing structural similarities. In addition, there could be both physical property and structural correlations between the different earth models, and these complexities cannot be captured by any existing joint inversion techniques. Note that, in practical applications, empirical or statistical correlations between different physical properties may exist, but their specific form may be unknown. In this situation, one can use a method of joint inversion, which does not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. This approach to the joint inversion of multimodal geophysical data uses Gramian spaces of model parameters and Gramian constraints, computed as determinants of the corresponding Gram matrices of the multi-modal model parameters and/or their attributes. This method, recently introduced by Zhdanov et al. (2012), has been shown to be a generalized method of joint inverting any number and combination of geophysical datasets, and includes extant methods based on correlations and/or structural constraints of the multiple physical properties as special case. The method is illustrated by two case studies. We present the results of joint inversion of airborne gravity gradiometer (AGG) and magnetic data collected by Fugro Airborne Surveys in the area of McFaulds Lake located in northwestern Ontario. We also jointly invert airborne magnetic and electromagnetic data from the Lac de Gras region of the Northwest Territories of Canada. These case studies demonstrate how joint inversion using Gramian constraints may enhance subsurface imaging of the mineral targets.