Stochastic estimation of facies using ground penetrating radar data

Moysey, S. M.; Caers, Jef; Knight, Rosemary; Allen‐King, Richelle M.

doi:10.1007/s00477-003-0152-6

Cited by 35 publications

(22 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[45] Given our understanding of the link between spatial bivariate statistics and the sedimentary architecture, it is clear how new data characterizing proportions and lengths of stratal unit types derived from quantitative outcrop studies [e.g., Dai et al, 2005], geophysical methods [Moysey et al, 2003], or new cores could be used directly in modeling spatial bivariate statistics. Being able to develop such correlation models using lithologic indicator data is advantageous.…”

Section: Discussionmentioning

confidence: 99%

Horizontal spatial correlation of hydraulic and reactive transport parameters as related to hierarchical sedimentary architecture at the Borden research site

Ritzi

Huang

Ramanathan

et al. 2013

Water Resources Research

Self Cite

View full text Add to dashboard Cite

[1] Highly resolved data from the Borden research site provide a unique opportunity to study the horizontal spatial bivariate correlation of hydraulic and reactive attributes affecting subsurface transport. The data also allow quantitatively relating this correlation to the hierarchical sedimentary architecture of the aquifer. The data include collocated samples of log permeability, Y, the log of the perchloroethene sorption distribution coefficient, Ä, and lithologic unit type. The horizontal Y and Ä autosemivariograms and the Ä-Y cross-semivariogram have the same underlying correlation structure (shape and range in the rise to a sill). The common structure is not due to Ä-Y point correlation or in-unit spatial correlation. The common structure is defined by how the proportion of lag transitions crossing different unit types (i.e., the cross-transition probability structure) increases with increasing lag distance. The common underlying cross-transition structure contains two substructures with different correlation ranges corresponding to two scales of unit types within the sedimentary architecture. For each substructure, a large standard deviation in the length of units relative to the mean length gives rise to an exponential-like shape and the proportions and mean length of units define the ranges. The horizontal Ä-Y spatial cross correlation is primarily defined by the larger-scale substructure and the differences in mean Ä and Y between larger-scale unit types.

show abstract

Section: Discussionmentioning

confidence: 99%

Horizontal spatial correlation of hydraulic and reactive transport parameters as related to hierarchical sedimentary architecture at the Borden research site

Ritzi

Huang

Ramanathan

et al. 2013

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…One data type is geophysical data which is more readily accessible and often can reveal whether the medium heterogeneity is adequately captured . The geophysical data used in geostatistical applications may include gamma ray [Miller et al, 2000;Cassiani and Binley, 2005], seismic [Yao, 2002], ground penetrating radar [Moysey et al, 2003;Kowalsky et al, 2005], and electrical resistance and hydraulic tomography [Yeh and Liu, 2000;Zhu and Yeh, 2005;Yeh and Zhu, 2007]. Different ways of using the geophysical data are warranted, depending on the relationships between the geophysical data and the variables of interest (e.g., the soil classes in this study).…”

Section: Introductionmentioning

confidence: 99%

A Markov chain model for characterizing medium heterogeneity and sediment layering structure

Khaleel

2008

Water Resources Research

View full text Add to dashboard Cite

[1] By leveraging use of ''soft'' data (e.g., initial moisture content, q i ), this study applies the transition probability (TP) based Markov chain (MC) model to sediment textural classes for characterizing the medium heterogeneity and sediment layering structure. The TP/MC method is evaluated by simulating the vadose zone moisture movement at a field site, where the stratigraphy consists of imperfectly stratified soil layers. Soil heterogeneity is characterized via spatial variability of the geometry of soil textural classes. When the q i measurements, which carry signature about medium heterogeneity and stratigraphy, are not included in the TP/MC model, it is not possible to identify the horizontal TP. The q i measurements, when transformed into soil classes, are necessary in mapping the soil layering structure prevalent at the site. The soil hydraulic parameters for each soil class are treated deterministically and are estimated on the basis of core samples. To evaluate uncertainty in characterizing geometry of the soil classes, multiple conditional realizations of the soil classes are generated. A Monte Carlo simulation shows that the simulated mean moisture contents agree well with corresponding field observations. The observed splitting of the moisture plume in a coarse sand layer that is sandwiched between two fine-textured layers, the southeastward movement of the plume during the redistribution period, and the near-zero fluid flux below the bottom fine layer are adequately simulated. Spatial variability of the field-measured moisture content is sufficiently captured by the 95% confidence intervals calculated from the Monte Carlo simulations. Investigating the effect of data conditioning on the simulated results shows that a reduction of conditioning data does not necessarily deteriorate simulation results if other conditioning data exist within the mean length of the soil classes. The TP/MC method is flexible so that other types of site characterization data (e.g., geophysical data) can be incorporated as they become available.Citation: Ye, M., and R. Khaleel (2008), A Markov chain model for characterizing medium heterogeneity and sediment layering structure, Water Resour. Res., 44, W09427,

show abstract

“…In our work we have used artificial neural networks as a tool to improve the interpretation of radar images into radar facies models. This research forms part of the Ph.D. thesis of Stephen Moysey, graduate student in the Geophysics Department, Stanford University and is described in Moysey and Knight (2003b).…”

Section: Can We Use the Spatial Distribution Of Radar Reflections As mentioning

confidence: 99%

“…Accurate reproduction of the large-scale architecture of the subsurface is a critical step in building a model when prediction of contaminant fate and transport are of interest. Therefore, a major contribution of our work has been the use artificial neural networks as a means to quantify the uncertainty in the interpretation of a radar image to obtain a radar facies model (Moysey et al, 2003b).…”

Section: Radar Facies Analysismentioning

confidence: 99%

“…Another major advantage of our stochastic approach is that we are able to propagate the uncertainty in the resulting radar facies interpretations to estimates of hydrologic processes, such as flow and transport. These ideas are illustrated in Moysey et al (2003b) for a simple conservative contaminant transport problem in a medium composed of two distinct facies types. In general, we find that the relative worth of radar compared to other data and the resulting reduction of uncertainty obtained will depend on the quality of the radar data available, which is highly dependent on the geologic environment.…”

Section: Radar Facies Analysismentioning

confidence: 99%

See 1 more Smart Citation

The Use of Radar Methods to Determine Moisture Content in the Vadose Zone

Knight¹

2003

View full text Add to dashboard Cite

Stochastic estimation of facies using ground penetrating radar data

Cited by 35 publications

References 9 publications

Horizontal spatial correlation of hydraulic and reactive transport parameters as related to hierarchical sedimentary architecture at the Borden research site

Horizontal spatial correlation of hydraulic and reactive transport parameters as related to hierarchical sedimentary architecture at the Borden research site

A Markov chain model for characterizing medium heterogeneity and sediment layering structure

The Use of Radar Methods to Determine Moisture Content in the Vadose Zone

Contact Info

Product

Resources

About