Spatial growth patterns are natural recording media (NRMs) that preserve important
historical information, which can be accessed and analyzed to reconstruct past …

To accurately reconstruct palaeoenvironmental change through time it is important to
determine which rock samples were deposited contemporaneously at different sites or …

Deterministic geophysical inversion suffers from a lack of realism because of the
regularization, while stochastic inversion allowing for uncertainty quantification is …

We present a new, fully probabilistic and nonlinear inversion method to estimate the spatial
distribution of geological properties (depositional facies, diagenetic rock types, or other rock …

A long standing problem in paleoceanography concerns the reconstruction of water
temperature from δ 18 O carbonate. It is problematic in the case of freshwater influenced …

The behaviors and skills of models in many geosciences (eg, hydrology and ecosystem
sciences) strongly depend on spatially-varying parameters that need calibration. A well …

Recovering geologically realistic physical property models by geophysical inversion is a
long-standing challenge. Generative neural networks offer a promising path to meeting this …

Although global circulation models (GCMs) have been used for the reconstruction of
precipitation for selected geological time slices, there is a lack of a coherent set of …

Multivariate compositional count data arise in many applications including ecology,
microbiology, genetics and paleoclimate. A frequent question in the analysis of multivariate …

Probabilistic inversion within a multiple‐point statistics framework is often computationally
prohibitive for high‐dimensional problems. To partly address this, we introduce and evaluate …