Geological models are key to our understanding of the subsurface by providing both visual and quantitative context. But accurately modeling the significant heterogeneities, discontinuities and the uncertainties of geological systems from often sparse data remains challenging. Substantial developments in geomodeling over the past years has helped bridge the gap between input data and resulting geomodel, allowing for the (semi-)automated construction of geomodels, a quicker model validation and rebuilding when new data arrives, as well as an efficient testing of multiple hypotheses. Increasing computing power now also allows for effective stochastic simulation of uncertainties in geomodeling, as well as the integration of probabilistic inference frameworks and geophysical inversions. Machine learning approaches can be used in every step of the geomodeling pipeline to enhance the process: from automated input data extraction and classification to probabilistic model selection.

We seek here contributions from all geoscientists using 3-D geological modeling methods, as well as novel developments to construct these models, to quantify and communicate uncertainties, highlighting existing challenges and future developments, including integrating geological modeling into geophysical inversions. Of special interest are also approaches to combine and enhance geomodeling with machine learning methods. Applications can be in any field of solid earth sciences to address scientific questions throughout the lithosphere or anthroposphere.