Introduction to Geostatistics: Applications in Hydrogeology

By Ted Chang. Technometrics, 2001

416 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/2f4d33514b54ac8c0c59863c34609257507ca59b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

In Ocean Engineering

Citations in the corpus

Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling (2004, methodology): “Stochastic approaches include geostatistical techniques [Snodgrass and Kitanidis, 1997; Michalak and Kitanidis, 2002, 2003, 2004] and the minimum relative entropy method [Woodbury and Ulrych, 1996; Woodbury et al., 1998; Neupauer et al., 2000].”
Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom (2009, neutral): “It is well known that when a large-scale trend dominates variability this ‘swamps’ the variogram (Kitanidis, 1997).”
A method for enforcing parameter nonnegativity in Bayesian inverse problems with an application to contaminant source identification (2003, methodology): “The method uses the method of images to define a prior probability density function based on reflected Brownian motion that implicitly enforces nonnegativity.”
Ecological Models and Data in R (2008, background): “Time-series models (Chatfield, 1975; Diggle, 1990; Venables and Ripley, 2002) and spatial models (Ripley, 1981; Cressie, 1991; Kitanidis, 1997; Haining, 2003; Venables and Ripley, 2002) also allow correlation between the error in different points, although often in a more restricted way than repeate”
Quantification of uncertainty in pedotransfer function‐based parameter estimation for unsaturated flow modeling (2009, methodology): “This section presents the propagation of the parameter estimation uncertainty through numerical simulation of the SL field experiment [Ye et al., 2007a] and the subsequent comparison to field measurements.”
Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce (2006, background): “…n o s s us i em T sam p qua T poi h ghts t rthe a but i i e nce o ation t D men o take t ues f y a fo decision making such as description of contaminated areas where amendment measures should be taken or areas of good soil quality where specific management plans can be developed (Kitanidis, 1997).”
A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields (2010, methodology): “…for example, onemay be interested in mapping the spatial distribution of the hydraulic conductivity K [cf. Kitanidis and Vomvoris, 1983; Kitanidis, 1986, 1991, 1995, 1997a, 1997b; Carrera and Neuman, 1986a, 1986b; Hernandez et al., 2006] using measurements of the hydraulic head,…”
Bayesian inversion for facies detection: An extensible level set framework (2009, methodology): “Pixel RMSE (m/s) Geostatistical unzoned NA 80.52 thresholdedc 13.40 74.43 SVD unzoned NA 82.27 thresholdedc 14.08 79.11 BLIP unknown b 6.32 64.12 known b 4.40 61.67 aGeostatistical analysis used linear variogram with parameters optimized using Kitanidis’ cR/Q2 criteria [Kitanidis, 1997].”
Accounting for the effect of transport errors on tracer inversions (2005, methodology): “We determined the correlation timescale ‘t and lengthscale in the horizontal (‘x) and vertical (‘z) using the variogram technique [Kitanidis, 1997].”