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GPR response and FDTD modeling to water and fuel infiltration in a sand box experiment

GPR response and FDTD modeling to water and fuel infiltration in a sand box experiment. by Maksim Bano Maksim.bano@eost.u-strasbg.fr Ecole et Observatoire des Sciences de la Terre (EOST), 5 Rue René Descartes, 67087 Strasbourg FRANCE. Outline Introduction

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GPR response and FDTD modeling to water and fuel infiltration in a sand box experiment

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  1. GPR response and FDTD modeling to water and fuel infiltration in a sand box experiment by Maksim Bano Maksim.bano@eost.u-strasbg.fr Ecole et Observatoire des Sciences de la Terre (EOST), 5 Rue René Descartes, 67087 Strasbourg FRANCE

  2. Outline • Introduction • What’s GPR? Effect of the frequency and humidity on the GPR data. • Presentation of GPR experiment in the lab • Experiment set up, data acquisition, comments on the measurements. • Water content estimation • Comparison with real water volume injected in the box. • Conclusions on Water Contents • Influence of the pollution (gasoil) on GPR data • Data acquisition, results, evolution of the pollution and FDTD modeling • Conclusions and Perspectives on Pollution

  3. Introduction

  4. GPR - Ground Penetrating Radar Principe of GPR Central Unity Amplitude Time (ns) Antennae Lap Top Wheel

  5. Acquisition. Common Mid-Point Data M In a Common Mid-Point (CMP) acquisition, antennae separation is increased about some central point. CMP Acquisition

  6. Effect of the frequencies used 50 MHz Antenna ‘Snake’ 250 MHz Shielded Antenna GPR images obtained with 50 (rough-terrain antenna ; “snake”) and 250 MHz Antennae, Finneidfjord Northern Norway

  7. a) b) Effect of the Humidity on GPR data Dry soil Humid soil The same profile acquired with 500 MHz antennae. a) in May 2006 and b) in October 2008

  8. Some important points • Water Content (volumetric): θw=Vw/Vtotal ;θw=φ.Sw • Relative dielectric permittivity(dielectric constant) =  /0 with 0 the permittivity of the free space. water=81; dry rocks:  = 3-5; humid rocks:  = 6-30. • Propagation velocity: v = c / 1/2 m/ns. c = 0,3 m/ns is the free space velocity.

  9. Presentation of GPR experiment in the lab

  10. Experiment set up 0,98 m 2 m Sand Box Sand box and injection system PVC Pipes Clay cake Steel pipe Steel ball

  11. Data acquisition Frequencies used: 900 and 1200 MHz Cross section of the sand box with the projection of the objects. Plane view of the sand box with measurement grid and different objects.

  12. 48 cm 72 cm • Four data set of measurements • Measurements on dry sand • Measurements with water level at 72 cm depth (26 cm thick) • Measurements with water level at 48 cm depth (48 cm thick) • Measurements after draining

  13. Results (1) TA T0 Steel P2? P03 P36 P56 APVC EPVC Steel

  14. Results (2) Central Profile (P36) with different saturation states Water level at 72 cm depth Dry sand Water level at 48 cm depth After draining

  15. Results (3) CMP and constant offset profiles (P1) with different saturation states Water level at 48 cm depth Dry sand

  16. Results (4) 3D GPR data sets a) b) B a) dry sand and b) the water level at 48 cm depth

  17. Estimation of water contents

  18. Relative dielectric Permittivity The determination of the average dielectric constants, for different depth, is performed from the propagation velocities (=c²/v²) :

  19. Water contents Relationships between water content  and relative dielectric permittivity : Topp Relationship (Topp et al., 1980)  = -5,3x10-2 + 2,92x10-2  - 5,5x10-4 2 + 4,3x10-6 3 CRIM Relationship (Mavko et al., 1998) Hanai-Bruggemann-Sen Relationship (Hanai, 1968) et

  20. Water Quantities The water quantities (in liters) estimated (in whole box) by using the previous relationships Water quantity (liter)

  21. Variations of water quantities Estimates of the amounts of water (in liter) injected in the sand box (for different saturation cases) as obtained using the Topp, CRIM and HBS equations. V1 is the amount of water for the data set with the water table at 72 cm depth, minus that of the dry sand case; V2 is the amount of water for the data set with the water table at 48 cm depth, minus the amount of water for the dry sand case.

  22. Water Quantity (liter) Variations of water quantities In each case we underestimated the variation in the amount of water in the sand box using GPR, but the final results are very close to the amount of water injected.

  23. Conclusions on Water Contents GPR is an effective method to assess and monitor water in the case of a vadose zone. By repeating the same GPR measurements over a controlled vadose zone (sand box experiment), one can compare and calibrate the water content obtained from GPR measurements with the actual water content present in the soil. The water variations are underestimated (by the three relationships) but the final results were very close to the amount of water injected.

  24. Influence of a pollution (gasoil) on GPR data

  25. Injection point of the gasoil Data acquisition 2nd Experiment: After drainage we let the sand box resting (two months) and performed measurements in April 2004 (this state is considered as ‘dry’). Measurements with water level at 72 cm depth (26 cm thick, 240 l) in may 2004 We injected 100 l of fuel (gasoil) and repeated measurements in may 2004 and June 2004 .

  26. Influence of the gasoil (1) The trace 40 Profile T0 before injection Profile T0 after injection

  27. B B B Influence of the gasoil (2) Two CMPs acquired after fuel injection. a) CMP16 above the steel ball P1 and b) CMP56 above the steel ball P2. B indicates the reflections from the bottom.

  28. b) a) Laterally extension of the plume pollution Travel time of the reflections from the bottom of the box. a) Before fuel injection b) After fuel injection

  29. 2 m 1,40 m Dry sand,  = 4,6 (h=32 cm) Capillary Fringe (h=32 cm) Sand saturated with gasoil,  = 3,8 Steel pipe Sand saturated with water (h=35 cm) Basement of the sand box (air, wood and sand) Sand mixted with air and gasoil,  = 4 Sand mixted with water and gasoil,  = 15 Modeling of GPR data by FDTD 0,98 m Model used for modeling of profile T0 12 days (in May 2004) after fuel injection.

  30. Modeling by FDTD Modeled profile T0 Real profile T0

  31. R Evolution of the pollution in time Profile T0 May 2004 June 2004

  32. R R Evolution of the pollution in time profile P56. May 2004 June 2004 Trace 19

  33. 2 m 1,4 m 0,98 m Level of a saturated sand,  = 45 Modeling of GPR data by FDTD Model used to follow the evolution of the profile T0 45 days after injection

  34. R R R Modeling by FDTD Profile T0 modeled Profile T0 real

  35. Conclusions and Perspectives The GPR data do not show any clear reflections from the plume pollution, however GPR velocities are extremely affected by the presence of the fuel. The laterally extension of the plume pollution in the vadose zone is shown by plotting the travel times of the reflection from the bottom of the sand box. It seems that pore water has been replaced by the fuel through a lateral flow by creating a high saturated zone far from the fuel injection point. The forward FDTD modeling method gave theoretical support to explain the origin of the observed reflections from the contaminated vadose zone. Perspective: To follow the lateral flow of the plume, a joint GPR and lateral flow modeling is necessary.

  36. Thank you for your attentionMaksim.bano@eost.u-strasbg.fr

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