1. 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

2. 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

3. 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!

4. 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

5. 10’s of cm’s to 100’s of meters ?

7. ? ? ? ?

8. ?

9. 12 m 52 m 10 m Knoll et al. (1988)

10. geophysical properties

11. hydrogeologic information transform

12. Develop a model of large-scale architecture.

14. depth 20 meters TxRx change in dielectric properties 11 22

15. Sandy Point spit, Alberta (Smith and Jol, 1992)

16. Develop a large-scale model using radar facies.

17. Radar facies are defined by patterns shapes, bounding surfaces internal “texture” Smith and Jol (1992) Develop a large-scale model using radar facies.

18. Sandy Point spit, Alberta (Smith and Jol, 1992) radar facies 1 radar facies 2 radar facies 3

19. Sandy Point spit, Alberta (Smith and Jol, 1992) radar facies 1 radar facies 2 radar facies 3 Radar facies = lithofacies/hydrofacies?

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

21. 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

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

23. Step #1- training (i.e. calibration) with a known data set: radar attributes (e.g., reflection dip, continuity) facies probabilities P(F=f 1 ) = 0 P(F=f 2 ) = 1 P(F=f 3 ) = 0 Neural Networks: Lithofacies Recognition InputsWeights and combinations wells, cores

24. Neural Networks: Lithofacies Estimation Neural network used to assign facies probabilities at each location based on local patterns. radar attributes (e.g., reflection dip, continuity) facies probabilities P(F=f 1 )=.7 P(F=f 2 )=.2 P(F=f 3 )=.1

25. Lithofacies Probabilities 01 Facies 1 Facies 4Facies 3 Facies 2 Probabilities allow us to include uncertainty in modeling

26. Use neural net to interpret facies assuming that training remains valid for all other data sets

27. 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?

28. 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?

29. Conditional Facies Realizations Radar Data Facies (NN) Facies 1 Facies 2 Direct Facies Observations (e.g., well data) 0 1 GEOSTATISTICS Use direct observations + radar data to develop models of large-scale architecture.

30. Application - Borden Groundwater Research Site

32. ? ? ? ?

33. ? 050100150200250m Can we use radar data to fill in between and beyond core samples?

34. 450 MHz radar data: 17 NS lines, 17 EW lines; depth ~3m 12 core samples in top 1.5 m

35. 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).

36. 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).

37. 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).

38. 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).

39. 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).

40. 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

41. Core depth converted to time using velocity of 0.69 m/ns N Time (ns) Distance (m) Core Data Only

42. 17 north-south GPR lines imported as data cube Frequency: 450 MHz Length: 20 m Spacing: 1/8 m Radar Data with Cores

43. Moving through the 3D Volume

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

48. 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 Three Main Horizons Identified 1 2 3

49. 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

50. Neural Network Classification Results maximum likelihood 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 Geoprobe Horizons

51. Neural Network Classification Results maximum likelihood 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 Geoprobe Horizons

52. 12 m 52 m 10 m Knoll et al. (1988)