Probabilistic modelling and mapping with electromagnetic data using pre-computed geological look-up tables as prior information

Deterministic inversion of electromagnetic (EM) data yields a single best fitting resistivity model of the subsurface, which can be used to interpret the geological subsurface. Such a model fails to capture the uncertainty in both resistivity and geology. This limitation is critical, as multiple, geologically dissimilar subsurface configurations can yield equivalent EM responses, meaning a single model representation can be inaccurate or even misleading. Probabilistic inversion of the EM data provides a principled solution by characterizing the range of subsurface models consistent with data, and thereby explicitly quantifying uncertainty in both the geophysical and geological models.

Here we invert by rejection sampling of pre-computed geophysical and geological 1D prior models. This allows for fast and efficient probabilistic inversion of large-scale EM surveys containing thousands of soundings. An added benefit of using pre-computed geological models is the possibility to encode geological expert knowledge into the models as direct information. In this context expert knowledge can be many things, for example the resistivity-lithology relationship, the chronological sequence of geological units, or the relative occurrence of various lithologies to name a few.

In the presentation, we demonstrate how such a probabilistic inversion workflow can be set up and applied on towed transient EM data from geophysical surveys in varying geological settings. The required inputs are (i) a geophysical dataset consisting of EM soundings, and (ii) an expert-based assessment of the plausible geological subsurface architectures in the survey area. Optionally, geophysical and lithological well logging can be used to further constrain the inversion. We will highlight the tool/software (GeoPrior1D) we have developed to construct prior ensembles with encoded geological knowledge, especially suited for such a workflow. GeoPrior1D is an open-source tool for generating ensembles of one-dimensional geological and geophysical models that explicitly represent prior models for probabilistic inversion problems.

Finally, we present key outcomes of the probabilistic modelling. This includes resistivity models with uncertainty, lithological models with uncertainty (entropy), class probabilities, and various themed maps. The produced models and maps, always accompanied by rigorously quantified uncertainties, enable better and more reliable decision-making across applications such as geohazard risk assessment, resource volume estimates, groundwater modelling, and much more.