02 Feb 2023
 | 02 Feb 2023
Status: this preprint is currently under review for the journal HESS.

Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties

Falk Heße, Sebastian Müller, and Sabine Attinger

Abstract. The geostatistical characterization of the subsurface is confronted with the double challenge of large uncertainties and high exploration costs. Making use of all available data sources is consequently very important. Bayesian inference is able to mitigate uncertainties in such a data scarce context by drawing on available background information in form of a prior distribution. To make such a prior distribution transparent and objective, it should be calibrated against a data set containing estimates of the target variable from available sites. In this study, we provide a collection of covariance/variogram functions of the subsurface hydraulic parameters from a large number of sites. We analyze this data set by fitting a number of widely used variogram model functions and show how they can be used to derive prior distributions of the parameters of said functions. In addition, we discuss a number of conclusions that can be drawn for our analysis and possible uses for the data set.

Falk Heße et al.

Status: open (until 30 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-15', Anonymous Referee #1, 22 Feb 2023 reply
  • RC2: 'Comment on hess-2023-15', Anonymous Referee #2, 12 Mar 2023 reply
  • RC3: 'Comment on hess-2023-15', Shlomo P. Neuman, 14 Mar 2023 reply

Falk Heße et al.

Falk Heße et al.


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Short summary
In this study, we have present two different advances for the field of subsurface geostatistics. First, we present data of variogram functions from a variety of different locations around the world. Second, we present a series of geostatistical analyses aimed at examining some of the statistical properties of such variogram functions and their relationship to a number of widely used variogram model functions.