Quantitative integration of high-resolution hydrogeophysical data:a novel approach to Monte-Carlo-type conditional stochastic simulations and implications for hydrological predictions

摘要

Geophysical techniques can help to bridge the gap that exists with regard to spatial resolution and coverage for classical hydrological methods.This has lead to the emergence of new and rapidly growing research domain generally referred to as hydrogeophysics.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 notoriously site-specific nature of petrophysical parameter relations,the fundamental usefulness of multi-method hydrogeophysical 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 database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data.In this contribution,we present a novel approach towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for high-resolution and high-quality datasets.Monte-Carlo-based optimization 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.Compared to more conventional approaches of this kind,our novel approach provides significant advancements in the way that large-scale structural information from the hydrogeophysical data can be accounted for,which represents an inherently problematic,and as of yet unresolved,aspect of Monte-Carlo-type conditional simulation techniques.We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure.Our procedure is first tested on pertinent synthetic data,and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site near Boise,Idaho,USA.Finally,we compare the performance our approach to hydrogeophysical data integration to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media.