Probabilistic Evaluation of Structural Uncertainty in a Synthetic Geological Benchmark Using GeoBUS

Reliable characterization of subsurface geology is a key prerequisite for reducing uncertainty in geoscientific studies and for lowering costs and risks in geothermal drilling. In this study, we apply GeoBUS (Geological modeling by Bayesian Updating of Scalar fields), a probabilistic structural geological modeling workflow, to a synthetic benchmark that represents the characteristic succession of geological units in the canton of Thurgau, Switzerland. As an initial test case, we construct a representative one-dimensional geological model based on available legacy data.

In the first step, a geological prior model is created by introducing epistemic structural uncertainty, perturbing the depths of geological interface points within predefined bounds. For each realization, implicit geological modeling is performed using radial basis function interpolation, resulting in an ensemble of scalar fields from which geological interfaces are represented as isolines of common scalar values.

In a second step, we calculate synthetic surface-wave dispersion curves based on the geological models using representative literature values and considering uncertainties and model variations. The dispersion curves are then inverted for subsurface velocity profiles to estimate biases and resolution limits of inversion schemes compared to the ground truth. We will test an ensemble of plausible subsurface models that is consistent with the dispersion data rather than as a single deterministic solution.

In the third step, literature-based seismic velocities are assigned to the geological units in the prior ensemble of geological model to enable comparison with the seismic data inversion results. An ensemble-based Bayesian update step is then applied to the scalar field ensemble, resulting in a posterior ensemble that is consistent with the assimilated seismic information. By evaluating each scalar field to derive geological interfaces, we obtain a posterior ensemble of geological models that consistently integrates information from both geological modeling and geophysical inversion and allows structural uncertainty to be quantified.

Using a synthetic example, we assess the performance of the GeoBUS workflow with respect to (1) the structural uncertainty in the geological model and (2) the value of information contained in the seismic data, including the influence of measurement sensitivity and prior constraints that may lead to updates in model regions weakly constrained by the assimilated seismic data. Validating the approach in this controlled one-dimensional setting provides an essential benchmark before extending the study to higher-dimensional and more complex geological settings.

This work was funded by the European Union’s Horizon Europe Framework Programme for Research and Innovation under the GeoHEAT project (Grant Agreement No. 101147571)