The Solid Earth Physics group 

Part of Climate and Computational Geophysics, Niels Bohr Institute, University of Copenhagen 
 

Effective highresolution Geological ModelingThis project is a crossdisciplinary applied/theoretical project funded from September 2013 by The Danish National Research Foundation (Højteknologifonden) (DTU Budget DKK 2,390,400).SummaryIn applied geoscience, methods for combining geoinformation and setting up a geological model is today typically sequential: Each type of available geoinformation is treated separately, by experts in each individual type of geodata. A typical scenario related to setting up models of groundwater reservoirs is the following: A geophysicist may use inverse theory to translate geophysical data into a (most often smooth) model of physical properties of the subsurface. This model is then used by a geological expert to suggest a geological model, which may then again be used by for example a hydrologist as the basis for setting up a flow model of the subsurface.
There are several  currently unsolved  problems related to this practice which can be summarized as follows:
1. The amount of geodata, from which the geological model is built, is constantly increasing. Already today, the amounts of geodata are so great that in practice not all available geodata are used to setup geological models. To overcome these challenges this project aims at formulating the problem of integration of geoinformation in a statistical Bayesian framework, where all information is quantified by statistical models. Based on state of the art methods developed in inverse problem theory and geostatistics, we aim to develop new theory and applications that can be used to solve the challenges listed and, at the same time, be manageable in practice. This will enable efficient geological interpretation of possibly very large geophysical data sets, and allow inclusion of geologically realistic small scale variability. Combining these methodologies will increase reliability and usability of geomodels when used in water resource evaluation and risk analysis. (Collaborators: IGIS, Denmark; GEUS)  
