Quantitative Integration of Hydrogeophysical and Hydrological Data: Geostatistical Approaches

Geophysical techniques can help to bridge the rather broad gap that exists with regard to resolution and coverage for classical hydrological methods. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range as well as the inherently site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting generally vast and often diverse databases in order to obtain a unified model of the probed subsurface region that is internally consistent with the entire available database. In this chapter, we review two approaches towards hydrogeophysical data integration that we consider to be particularly suitable and promising as well as largely complementary in their purposes: cluster analysis and Monte-Carlo-type conditional stochastic simulation. Cluster analysis allows for detecting systematic interrelations between various parameters and, based on this information, for producing internally consistent zonations of the target region. Under certain conditions, some of these techniques also allow for a robust and efficient reconstruction of the distribution of the petrophysical target parameters. An entirely different approach to hydrogeophysical data integration is based on Monte-Carlo-type conditional stochastic simulations. These techniques are immensely flexible and versatile, allow for accounting for a wide variety of data and constraints of vastly differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions.