GNGTS 2022 - Atti del 40° Convegno Nazionale

GNGTS 2022 Sessione 3.3 531 Figure 3. a) Coefficient of variation associated with ρ 0 . b) Coefficient of variation associated with Δρ . Conclusion. In this work, we implemented a Time-Lapse EB algorithm to invert TL-ERT data for monitoring purposes. This stochastic approach provides ensemble of posterior realisations pertaining to both the resistivity model at t 0 and the resistivity variation in time. From this ensemble of models, the uncertainties on the retrieved solution can be numerically assessed. Thus, the main advantage of our approach is the assessment of the statistical properties of the inverse problem solution. Due to the broad range of the subsurface resistivity, we evaluate the uncertainties through the coefficient of variation which is independent on the order of magnitude of the mean. The application to Pillemark field data demonstrates the reliability of the Time-Lapse EB in detecting the resistivity variation in time with a satisfying data prediction. The mean posterior variation of resistivity allows to identify the change in aquifer saturation in time. In addition, the retrieved uncertainties provide meaningful information about the low sensitivity side of the model. The comparison with the cascaded inversion confirms the reliability of the estimated posterior mean models. The next step of our research is to apply the algorithm to other monitoring sites and to compare it with other approaches. References Aleardi M., Vinciguerra A., and Hojat A.; 2021: Ensemble-Based Electrical Resistivity Tomography with Data and Model Space Compression. Pure and Applied Geophysics, 178 , 1781–1803. Descloitres M., Ruiz L., Sekhar M., Legchenko A., Braun J.J., Mohan Kumar M.S., and Subramanian S.; 2008: Characterization of seasonal local recharge using electrical resistivity tomography and magnetic resonance sounding. Hydrological Processes, 22 (3), 384–394. Günther T., Rücker C., and Spitzer K.; 2006: Three-dimensional modelling and inversion of dc resistivity data incorporating topography - II. Inversion. Geophysical Journal International, 166(2), 506-517. Høyer A.S., Klint K., Fiandaca G., Maurya P., Christiansen A., Balbarini N., Bjerg P., Hansen T., and Møller I.; 2019: Development of a high-resolution 3D geological model for landfill leachate risk assessment. Engineering Geology, 249 , 45–59. Labreque D.J. and Yang X.; Difference inversion of ERT data: a fast inversion method for 3-D in-situ monitoring. Environ. Eng. Geophys. 2001, 6 , 83-89. Maurya P.K., Rønde V.K., Fiandaca G., Balbarini N., Auken E., Bjerg P.L. and Christiansen A.V.; 2017: Detailed landfill leachate plume mapping using 2D and 3D electrical resistivity tomography - with correlation to ionic strength measured in screens.  Journal of Applied Geophysics , 138 , 1-8. Miller C.R., Routh P.S., Brosten T.R. and McNamara J.P.; 2008: Application of time-lapse ERT imaging to watershed characterization. Geophysics 73 , G7–G17. Rücker C., Günther T. and Wagner F.M.; 2017: pyGIMLi: An open-source library for modelling and inversion in geophysics. Computers and Geosciences, 109 , 106–123. Vinciguerra A., Aleardi M., Hojat A., Loke M.H. and Stucchi E.; 2022: Discrete Cosine Transform for Parameter Space Reduction in Bayesian Electrical Resistivity Tomography. Geophysical Prospecting, 70 , 193-209.