Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations


Serval ID
Inproceedings: an article in a conference proceedings.
Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations
Title of the conference
First International Conference on Frontiers in Shallow Subsurface Technology, Delft, The Netherlands
Dafflon B., Irving J., Holliger K.
Delft University of Technology, European Association of Geoscientists & Engineers
Publication state
Issued date
Geophysical techniques can help to bridge the inherent gap with regard
to spatial resolution and the range of coverage that plagues classical
hydrological methods. This has lead to the emergence of the new and
rapidly growing field of hydrogeophysics. Given the differing sensitivities
of various geophysical techniques to hydrologically relevant parameters
and their inherent trade-off between resolution and range 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 vast and diverse database in order to
obtain a unified model of the probed subsurface region that is internally
consistent with all available data. To address this problem, we have
developed a strategy towards hydrogeophysical data integration based
on Monte-Carlo-type conditional stochastic simulation that we consider
to be particularly suitable for local-scale studies characterized
by high-resolution and high-quality datasets. Monte-Carlo-based optimization
techniques are flexible and versatile, allow for accounting for a
wide variety of data and constraints of 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 approach provides significant advancements in the
way that the larger-scale deterministic information resolved by 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 corresponding field
data collected at the Boise Hydrogeophysical Research Site near Boise,
Idaho, USA.
Create date
25/11/2013 17:31
Last modification date
20/08/2019 15:56
Usage data