• Applications of Diverse Data Combinations in Subsurface Characterization using D-optimality Based Pilot Point Methods (DBM)
  • Jung, Yong;Mahinthakumar, G.;
  • Water Resources Research Division, Korea Institute of Construction Technology;Department of Civil Engineering, North Carolina State University;
Abstract
Many cases of strategically designed groundwater remediation have lack of information of hydraulic conductivity or permeability, which can render remediation methods inefficient. Many studies have been carried out to minimize this shortcoming by determining detailed hydraulic information either through direct or indirect measurements. One popular method for hydraulic characterization is the pilot point method (PPM), where the hydraulic property is estimated at a small number of strategically selected points using secondary measurements such as hydraulic head or tracer concentration. This paper adopted a D-optimality based pilot point method (DBM) developed previously for hydraulic head measurements and extended it to include both hydraulic head and tracer measurements. Based on different combinations of trials, our analysis showed that DBM performs well when hydraulic head is used for pilot point selection and both hydraulic head and tracer measurements are used for determining the conductivity values.

Keywords: Subsurface characteristics;Pilot point methods;D-optimality;Diverse data sets;

References
  • 1. de Marsily, G., Lavedan, G., Boucher, M., and Fasanino, G., 1984, Interpretation of interference tests in a well field using geostatistical techniques to fit the permeability distribution in a reservoir model, Geostatistics for natural resources characterization, pt.2, D. Reidel Publishing Company.
  •  
  • 2. Franssen, H.H., Hernandez, J.G., and Sahuquillo, A., 2003, Coupled inverse modeling of groundwater flow and transport and the worth of concentration data, J. Hydrol., 281, 281-295.
  •  
  • 3. Gomez-Hernanez, J.J., Sahuquillo, A., and Capilla, Jose E., 1997, Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data-I. Theory, J. Hydrol., 203, 162-174.
  •  
  • 4. Harvey, C.F. and Gorelick, S.M., 1995, Mapping hydraulic conductivity:Sequential conditioning with measurements of solute arrival time, hydraulic head, and local conductivity, Water Resour. Res., 31(7), 1615-1626.
  •  
  • 5. Jung, Y., Ranjithan, R.S., and Mahinthakumar, G., 2011, Subsurface characterization using a D-optimality based pilot point method, J. Hydroinfor., 13(4), 775-792.
  •  
  • 6. Knopman, D.S. and Voss, C.I., 1987, Behavior of sensitivities in the one-dimensional advection-dispersion equation: Implications for parameter estimation and sampling design. Water Resour. Res., 23(2), 253-272.
  •  
  • 7. Kowalsky, M.B., Finsterle, S., Williams, K.H., Murray, C., Commer, M., Newcomer, D., Englert, A., Steefel, C.I., and Hubbard, S.S., 2012, On parameterization of the inverse problem for estimating aquifer properties using tracer data, Water Resour. Res., 48 (W06535), 1-25.
  •  
  • 8. LaVenue, A.M. and Pickens, J.F., 1992, Application of a coupled adjoint sensitivity and kriging approach to calibrate a groundwater flow model, Water Resour. Res., 28(6), 1543-1569.
  •  
  • 9. LaVenue, A.M., RamaRao, B.S., de Marsily, G., and Marietta, M.G., 1995, Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields Application, Water Resour. Res., 31(3), 495-516.
  •  
  • 10. Liang, L., Zeng, G., Guo, S., Wei, A., Li, X., Shi, L., and Du, C., 2010, Optimal solute transport in heterogeneous aquifer: Coupled inverse modeling, Inter. J. Environ. Pollu., 42(1-3), 258-269.
  •  
  • 11. Mishra, S. and Parker, J.C., 1989, Parameter estimation for coupled unsaturated flow and transport, Water Resour. Res., 25(3), 385-396.
  •  
  • 12. Medina, A., Carrera, J., and Galarza, G., 1990, Inverse modeling of coupled flow and solute transport problem, ModelCARE 90: Calibration and Reliability in Groundwater Modeling, IAHS Publ. 195.
  •  
  • 13. RamaRao, B.S., LaVenue, A.M., de Marsily, G., and Marietta, M.G., 1995, Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields 1. Theory and computational experiments, Water Resour. Res., 31(3), 457-493.
  •  
  • 14. Strecker, E.W. and Chu, W.-S., 1986, Parameter identification of a ground-water contaminant transport model, Ground Water, 24(1), 56-62.
  •  
  • 15. Sun, N.-Z. and Yeh, W.W.-G., 1990, Coupled inverse problems in groundwater modeling, 1. Sensitivity analysis and parameter identification, Water Resour. Res., 26(10), 2507-2525.
  •  
  • 16. Van Rooy, D., Keidser, A., and Rosbjerg, D., 1989, Inverse modeling of flow and transport, Groundwater Contamination, Third IAHS Scientific Assembly, IAHS Publ. No. 185.
  •  
  • 17. Wen, X.-H., Deutsch, C.V., and Cullick, A.S., 2002, Construction of geostatistical aquifer models integrating dynamic flow and tracer data using inverse technique, J. Hydrol., 255, 151-168.
  •  
  • 18. Woodbury, A.D., Smith, L., and Dunbar, W.S., 1987, Simultaneous inversion of hydrogeologic and thermal data, 1, Theory and application using hydraulic heat data, Water Resour. Res., 23(8), 1586-1606.
  •  
  • 19. Woodbury, A.D. and Smith, L., 1988, Simultaneous inversion of hydrogeologic and thermal data, 2, incorporation of thermal data, Water Resour. Res., 24(3), 356-372.
  •  

This Article

  • 2013; 18(2): 45-53

    Published on Apr 30, 2013

  • 10.7857/JSGE.2013.18.2.045
  • Received on Apr 9, 2013
  • Revised on Apr 22, 2013
  • Accepted on Apr 22, 2013