Stochastic Environmental Research and Risk Assessment

, Volume 27, Issue 4, pp 969–983

A methodology for quantifying the value of spatial information for dynamic Earth problems

  • Whitney J. Trainor-Guitton
  • Tapan Mukerji
  • Rosemary Knight
Original Paper

DOI: 10.1007/s00477-012-0619-4

Cite this article as:
Trainor-Guitton, W.J., Mukerji, T. & Knight, R. Stoch Environ Res Risk Assess (2013) 27: 969. doi:10.1007/s00477-012-0619-4


We develop a methodology for assessing the value of information (VOI) from spatial data for groundwater decisions. Two sources of uncertainty are the focus of this VOI methodology: the spatial heterogeneity (how it influences the hydrogeologic response of interest) and the reliability of geophysical data (how they provide information about the spatial heterogeneity). An existing groundwater situation motivates and in turn determines the scope of this research. The objectives of this work are to (1) represent the uncertainty of the dynamic hydrogeologic response due to spatial heterogeneity, (2) provide a quantitative measure for how well a particular information reveals this heterogeneity (the uncertainty of the information) and (3) use both of these to propose a VOI workflow for spatial decisions and spatial data. The uncertainty of the hydraulic response is calculated using many Earth models that are consistent with the a priori geologic information. The information uncertainty is achieved quantitatively through Monte Carlo integration and geostatistical simulation. Two VOI results are calculated which demonstrate that a higher VOI occurs when the geophysical attribute (the data) better discriminates between geological indicators. Although geophysical data can only indirectly measure static properties that may influence the dynamic response, this transferable methodology provides a framework to estimate the value of spatial data given a particular decision scenario.

List of symbols

\( {{\theta}} \)

Geologic Input Parameter (e.g. training image)


Index of training images


Total number of training images


Vector of Earth parameters


Index of realizations

\( T_{\theta i} \)

Total number of realizations for training image i


Aquifer vulnerability

\( \ell \)

Surface location


Decision alternative

\( g_{a} \)

Decision predictor (e.g. flow simulation)


Value: metric to define outcome of decision


Synthetic data


Soft probability (pre-posterior)

Ρ, ρ

Electrical resistivity




Decision alternative combinations

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Whitney J. Trainor-Guitton
    • 1
  • Tapan Mukerji
    • 2
  • Rosemary Knight
    • 3
  1. 1.Lawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Energy Resources EngineeringStanford UniversityStanfordUSA
  3. 3.Department of GeophysicsStanford UniversityStanfordUSA