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.
Hereweinvertbyrejection samplingofpre-computedgeophysicalandgeological1Dpriormodels.
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.
Wewill 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