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					The Use of Ground Penetrating Radar Data
            in the Development of
   Facies-Based Hydrogeologic Models


  Rosemary Knight, Elliot Grunewald, Richelle Allen-King,
            Stephen Moysey, David Gaylord
Groundwater flow & transport models:
   – Evaluate/manage drinking water supply
   – Evaluate groundwater susceptibility to
     contamination
   – Estimate societal/ecological impacts of
     contamination
   – Assign risk to prioritize remediation needs
Groundwater flow & transport models:
   – Evaluate/manage drinking water supply
   – Evaluate groundwater susceptibility to
     contamination
   – Estimate societal/ecological impacts of
     contamination
   – Assign risk to prioritize remediation needs

 Incorporating heterogeneous distributions
 of subsurface properties authentically will
 reduce uncertainty for all of these!
Groundwater flow & transport models:
   – Evaluate/manage drinking water supply
   – Evaluate groundwater susceptibility to
     contamination
   – Estimate societal/ecological impacts of
     contamination
   – Assign risk to prioritize remediation needs

 but we should do so in a way that allows
 us to quantify uncertainty
10’s of cm’s to 100’s of meters




             ?
    ?       ?
?       ?
?
                        12 m



10 m
       52 m
              Knoll et al. (1988)
geophysical properties
    geophysical properties

             transform

hydrogeologic information
Develop a model of large-scale architecture.
                       Tx   Rx
depth 20 meters




                  k1
                             change in dielectric properties
                  k2
Sandy Point spit, Alberta (Smith and Jol, 1992)
Develop a large-scale model using radar facies.




               Sandy Point spit, Alberta (Smith and Jol, 1992)
Develop a large-scale model using radar facies.




                                              Smith and Jol (1992)

       Radar facies are defined by patterns
           shapes, bounding surfaces
           internal “texture”
Sandy Point spit, Alberta (Smith and Jol, 1992)




                                                  radar facies 1
                                                  radar facies 2

                                                  radar facies 3
Sandy Point spit, Alberta (Smith and Jol, 1992)




                                                  radar facies 1
                                                  radar facies 2

                                                  radar facies 3



       Radar facies = lithofacies/hydrofacies?
  Radar facies are defined by:
             patterns

Use neural networks for pattern recognition.
       Radar facies are defined by:
                  patterns

    Use neural networks for pattern recognition.
More efficient

Allows us to generate stochastic models
& quantify uncertainty.

      Moysey, Knight, Caers, Allen-King, 2002
      Moysey, Knight, Jol, 2005
            Neural Networks: Lithofacies Recognition
Step #1- training (i.e. calibration) with a known data set:

                                     wells, cores
               Neural Networks: Lithofacies Recognition
Step #1- training (i.e. calibration) with a known data set:

                                     wells, cores




      radar                                          facies
 attributes                                          probabilities
      (e.g.,                                         P(F=f1) = 0
 reflection                                          P(F=f2) = 1
       dip,                                          P(F=f3) = 0
continuity)       Inputs Weights and combinations
                      Neural Networks: Lithofacies Estimation
            Neural network used to assign facies probabilities at each
              location based on local patterns.
                    American Fork River braid delta

                                     Distance (m)
                0     10   20   30   40   50   60   70   80   90   100
Depth (m)




                                                                              Depth (m)
            0                                                             0

            3                                                             3

            6                                                             6

            9                                                             9




      radar                                     (modified from Jol and Smith, 1992)       facies
 attributes                                                                               probabilities
      (e.g.,                                                                              P(F=f1)=.7
 reflection                                                                               P(F=f2)= .2
       dip,                                                                               P(F=f3)= .1
continuity)
Lithofacies
Probabilities           0                                   1
Facies 1                            Facies 2




Facies 3                            Facies 4




Probabilities allow us to include uncertainty in modeling
Use neural net to interpret facies assuming that
training remains valid for all other data sets
Use neural net to interpret facies assuming that
training remains valid for all other data sets

Can we develop training (classification schemes) that
are transferable?
Use neural net to interpret facies assuming that
training remains valid for all other data sets

Can we develop training (classification schemes) that
are transferable?

Is there a characteristic radar signature associated
with specific depositional environments?
Use direct observations + radar data to develop models of large-scale
architecture.

       Direct Facies Observations                        Radar Data
       (e.g., well data)

                                                       Facies (NN)
                           Facies 1
                           Facies 2

                                                            0     1

                           GEOSTATISTICS

     Conditional
     Facies
     Realizations
Application - Borden Groundwater Research Site
    ?
?           ?

        ?
Can we use radar data to fill in between and beyond
core samples?

     0     50   100       150   200   250m




                      ?
450 MHz radar data: 17 NS lines, 17 EW lines; depth ~3m
12 core samples in top 1.5 m
Lithofacies:

Soil, Fine-Grained Planar Cross-Stratified (FPXS),
Distinct Plane Laminated (DPL), Massive Coarse-Grained (MCG),
Massive Fine-Grained (MFG), Faint Plane Laminated (FPL),




 Low-Angle Planar Cross-Stratified (LPXS),
 High-Angle Planar Cross-Stratified (HPXS),
 Deformed Sand (DS), Cross-Stratified Sand (XSS),
 Complexly Cross-Stratified Sand (CPXS), Laminated Silt (ZM).
Lithofacies:

Soil, Fine-Grained Planar Cross-Stratified (FPXS),
Distinct Plane Laminated (DPL), Massive Coarse-Grained (MCG),
Massive Fine-Grained (MFG), Faint Plane Laminated (FPL),




 Low-Angle Planar Cross-Stratified (LPXS),
 High-Angle Planar Cross-Stratified (HPXS),
 Deformed Sand (DS), Cross-Stratified Sand (XSS),
 Complexly Cross-Stratified Sand (CPXS), Laminated Silt (ZM).
Lithofacies:

Soil, Fine-Grained Planar Cross-Stratified (FPXS),
Distinct Plane Laminated (DPL), Massive Coarse-Grained (MCG),
Massive Fine-Grained (MFG), Faint Plane Laminated (FPL),




 Low-Angle Planar Cross-Stratified (LPXS),
 High-Angle Planar Cross-Stratified (HPXS),
 Deformed Sand (DS), Cross-Stratified Sand (XSS),
 Complexly Cross-Stratified Sand (CPXS), Laminated Silt (ZM).
Lithofacies:

Soil, Fine-Grained Planar Cross-Stratified (FPXS),
Distinct Plane Laminated (DPL), Massive Coarse-Grained (MCG),
Massive Fine-Grained (MFG), Faint Plane Laminated (FPL),




 Low-Angle Planar Cross-Stratified (LPXS),
 High-Angle Planar Cross-Stratified (HPXS),
 Deformed Sand (DS), Cross-Stratified Sand (XSS),
 Complexly Cross-Stratified Sand (CPXS), Laminated Silt (ZM).
Lithofacies:

Soil, Fine-Grained Planar Cross-Stratified (FPXS),
Distinct Plane Laminated (DPL), Massive Coarse-Grained (MCG),
Massive Fine-Grained (MFG), Faint Plane Laminated (FPL),




 Low-Angle Planar Cross-Stratified (LPXS),
 High-Angle Planar Cross-Stratified (HPXS),
 Deformed Sand (DS), Cross-Stratified Sand (XSS),
 Complexly Cross-Stratified Sand (CPXS), Laminated Silt (ZM).
                  Radar Data and Cores
                Visualized with Geoprobe®




To explore the 3D continuity of core lithologies and radar horizons
To correlate lithological data with radar reflections
             Core Data Only




                                            Time (ns)
                                     N




Core depth converted to time using velocity of 0.69 m/ns
          Radar Data with Cores




     17 north-south GPR lines imported as data cube
Frequency: 450 MHz      Length: 20 m       Spacing: 1/8 m
Moving through the 3D Volume
Moving through the 3D Volume
Moving through the 3D Volume
  Identifying and Tracking Horizons




  “Seed point” specified on potential horizon (max or min)
EzTracker tool explores away from seed for similar waveform
Identifying and Tracking Horizons
    Three Main Horizons Identified

         1

         2
         3




Horizon 1 interpreted as base of soil layer
Horizon 2 interpreted as base of X-bedded sand
Horizon 3 interpreted as base of massive/laminated zone
         Training the Neural Network




Chose four descriptive facies based on core data and horizons
Trained neural net using facies map for a single profile

After training neural net used to classify entire set of radar data
 Neural Network Classification Results
                          maximum likelihood

               Geoprobe Horizons




Substantial agreement of classifications with cores and horizons
Soil layer and Cross-stratified units particularly well-identified
Less continuous classifications of the deep half of the image may
reflect lateral variation observed in cores at depth
 Neural Network Classification Results
                          maximum likelihood

               Geoprobe Horizons




Substantial agreement of classifications with cores and horizons
Soil layer and Cross-stratified units particularly well-identified
Less continuous classifications of the deep half of the image may
reflect lateral variation observed in cores at depth
                        12 m



10 m
       52 m
              Knoll et al. (1988)

				
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