Quasi-2-D Bayesian inversion of central loop transient electromagnetic data using an adaptive Voronoi parametrization

Abstract

SUMMARYCentral loop transient electromagnetic (TEM) data are often interpreted by conventional 1-D or quasi-2-D inversion techniques. For example, the lateral constrained inversion (LCI) is a powerful technique for quick interpretation of central loop TEM data, and can produce spatially consistent resistivity images for profile data by assuming spatial correlation between adjacent model parameters. Such quasi-2-D techniques are very powerful in cases multidimensional effects are small or negligible. However, the inverse solution of conventional LCI methods strongly depends on subjective interpreter choices such as the model regularization and the imposed lateral constraints. Due to inherent non-linearity and nonuniqueness of the TEM inverse problems, this can result in biased model parameters and their estimated model uncertainties. We present a transdimensional Markov chain Monte Carlo method for the quasi-2-D inversion of TEM data using a Bayesian inference framework. We term the approach quasi-2-D, since the model is parametrized in 2-D with unstructured Voronoi cells, whereas the TEM response at each station is predicted using a 1-D forward solution to make the problem computationally affordable. During the inversion, the number of Voronoi cells as well as their positions and resistivities are variable. Accordingly, the level of model complexity is automatically determined by the framework and adapted to the spatial resolution of the data, thus avoiding the need for subjective model regularization or spatial constraints. The approach is validated using synthetic data and compared to 1-D Bayesian and conventional Gauss Newton inversion techniques. The application to dense field data from a floating TEM survey leads to a consistent subsurface image with unbiased uncertainty estimates and a plausible depth of investigation. The quantitative uncertainty information provided by the Bayesian framework is beneficial in identifying resolution.