Detecting saline water plume in a heterogeneous synthetic aquifer through a combination of POD and geoelectrical surveys

Document Type : Research paper

Authors

1 Assistant Professor, Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan Iran

2 MSc student of Geophysics, Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

Abstract

In recent decades, geoelectrical surveys have been progressively used to capture the geometry and evolution of contaminant plumes in groundwater systems. In this study, we examine a procedure of a combination of proper orthogonal decomposition (POD) and geoelectrical forward and inversion models to map the salinity plume inside a heterogeneous synthetic aquifer through the surface resistivity data. Here, we improve the framework presented by Oware et al. (2013) in which they used POD as part of the geoelectric inversion stage needing a higher memory and time to run as well as using a finite difference approach. More importantly, the center of mass of the final modeled plume obtained from their methodology required to be shifted to the mass center of the original plume, resulting in the inapplicability of the method for the real cases in which the mass center of the plume is still unknown before modeling. Since the POD method is separately performed prior to the geoelectrical models and also solved through the finite-element rather than the finite-difference approach, the presented procedure, in addition to decrease the required RAM capacity, can correctly capture the spatial distribution and geometry of the salinity plume without the need for shifting the mass center of the modeled plume, so that, it can be reasonably used for real cases. Moreover, the findings from the three different scenarios of salinity injection (single injection point on the border, single injection point somewhere inside the aquifer, and two injection points somewhere inside the aquifer) show that the border effect may cause a horizontal shift in the modeled plume compared to the reference case. Additionally, the results indicate that the strength of the method for detecting the geometry of the plume decreases with depth.

Keywords

References

خالقی، ف.، حیدریان، م.ح.، فاتح دیزجی، ع.، 1397. مکان‌یابی مناطق مستعد آب زیرزمینی در واحدهای آذرآواری با روش ژئوالکتریک (مطالعه موردی منطقه کال واقع در جنوب دماوند). هیدروژئولوژی، 3(2): 82-94. 
طاهری تیزرو، ع.، عابدینی، ش.، کمالی، م.، 1396. برآورد پارامترهای هیدرولیکی لایه‌های آبدار با روش ژئوالکتریکی الکتریک (مطالعه موردی: دشت چهاردولی، استان کردستان). هیدروژئولوژی، 2(1): 85-101.
Archie, G.E., 1942. The electrical resistivity logs as an aid determining some reservoir characteristics. Trans. AIME, 146: 54–61.
Binley, A., Hubbard, S.S., Huisman, J.A., Revil, A., Robinson, D.A., Singha, K., Slater, L.D., 2015. The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales. Water Resour. Res, 51: 3837–3866.
Bechtold, M., Vanderborght, J., Weihermüller, L., Herbst, M., Günther, T., Ippisch, O., Kasteel, R., Vereecken, H., 2012. Upward transport in a three-dimensional heterogeneous laboratory soil under evaporation conditions. Vadose Zone Journal, 11(2): vzj2011.0066.
Caterina, D., Hermans, T., Nguyen, F., 2014. Case studies of incorporation of prior information in electrical resistivity tomography: Comparison of different approaches. Near Surf. Geophys., 12: 451–465.
Deutsch, C.V., Journel, A.G., 1992. GSLIB: Geostatistical Software Library and User's Guide. Oxford University Press, New York.
Hermans, T., Oware, E., Caers, J., 2016. Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data. Water Resources Research, 52(9): 7262-7283.
Hermans, T., Nguyen, F., Klepikova, M., Dassargues, A., Caers, J., 2018. Uncertainty quantification of medium‐term heat storage from short‐term geophysical experiments using Bayesian evidential learning. Water Resources Research, 54(4): 2931-2948.
Linde, N., Doetsch, J., 2016. Joint inversion in hydrogeophysics and near-surface geophysics. In: Moorkamp, M., Leli evre, P.G., Linde, N., Khan, A. (Eds.), Integrated Imaging of the Earth: Theory and Applications. John Wiley & Sons, Inc, pp. 117–135.
Nguyen, F., Kemna, A., Robert, T., Hermans, T., 2016. Data-driven selection of the minimum-gradient support parameter in time-lapse focused electric imaging. Geophysics, 81(1): A1–A5.
Oware, E. K., Moysey, S. M. J., Khan, T., 2013. Physically based regularization of hydrogeophysical inverse problems for improved imaging of process‐driven systems. Water Resources Research, 49(10): 6238-6247.
Reynolds, J.M., 2011. An introduction to applied and environmental geophysics. John Wiley & Sons.
Rezaei, A., Mousavi, Z., Khorrami, F., Nankali, H., 2020. Inelastic and elastic storage properties and daily hydraulic head estimates from continuous global positioning system (GPS) measurements in northern Iran. Hydrogeology Journal, 28(2): 657-672.
Rücker, C., Günther, T., Wagner, F.M., 2017. pyGIMLi: An open-source library for modelling and inversion in geophysics. Computers & Geosciences, 109: 106-123.
Sulzbacher, H., Wiederhold, H., Siemon, B., Grinat, M., Igel, J., Burschil, T., Günther, T., Hinsby, K., 2012. Numerical modelling of climate change impacts on freshwater lenses on the North Sea Island of Borkum using hydrological and geophysical methods. Hydrol. Earth Syst. Sci., 16: 3621–3643.
Telford, W.M., Telford, W.M., Geldart, L.P., Sheriff, R.E., Sheriff, R.E., 1990. Applied geophysics. Cambridge university press.
Vanderborght, J., Kemna, A., Hardelauf, H., Vereecken, H., 2005. Potential of electrical resistivity tomography to infer aquifer transport characteristics from tracer studies: A synthetic case study, Water Resour. Res., 41: W06013.
Weiss, J., 2019. A tutorial on the proper orthogonal decomposition. In AIAA Aviation 2019 Forum (p. 3333).
Whitaker, S., 1986. Flow in porous media I: a theoretical derivation of Darcy's law. Transp. Porous Media. 1: 3-25.
Hydrogeology
Volume 6, Issue 2
Publisher
March 2022
Pages 42-51

Files

Share

How to cite

Statistics