The investigation of the subsurface has becoming of increasing interest to mitigate environmental risks. The development of Electrical Resistivity Tomography (ERT) monitoring station systems has triggered the application of Time-Lapse ERT technique to detect the resistivity variations in time. Indeed, the resistivity change can be associated with subsurface processes such as groundwater recharge (Descloitres et al., 2018) and aquifer contamination (Maurya et al., 2017). The ERT data inversion is an ill-posed, non-linear problem that it is usually solved through a deterministic least-square algorithms. Nevertheless, this approach is prone to get stuck in local minima of the error function. A strategy to tackle this issue is to apply stochastic inversion algorithms that consider the model parameters as random variables and the subsurface model as a realisation of a probability density function. However, the main drawback of this approach is the computational burden related to the several forward model evaluations needed to sample the parameter space (Vinciguerra et al., 2022). The Ensemble Based (EB) algorithm is an iterative data assimilation method that assimilates the observed data multiple times with an inflated covariance matrix (Aleardi et al., 2021). The result of this algorithm is an ensemble of realisations from which an approximation of the posterior probability density function (ppd) can be numerically assessed. This algorithm requires less computational time compared to standard Monte Carlo Markov Chain algorithms due to the lower number of forward modelling runs to estimate the ppd. (Aleardi et al., 2021). Nowadays, there exists different Time-Lapse inversion strategies: independent approach in which the data are inverted independently; the cascaded inversion, in which the inversion result of the first dataset is considered as starting model of the second dataset (Miller et al., 2008); or strategies such as the difference inversion that attenuate the effects of the systematic error during the acquisition (Labreque et al., 2001). To perform a Time-Lapse inversion, we rearrange the EB algorithm to obtain the posterior mean model, its variation in time and the uncertainties affecting the solution. In this work, we apply the Time-Lapse EB algorithm to field data acquired by a landfill monitoring station in Pillemark (Samsø, Denmark).
A Stochastic Ensemble Based approach for Time-Lapse ERT inversion
Vinciguerra A.
;Aleardi M.;Stucchi E.
2022-01-01
Abstract
The investigation of the subsurface has becoming of increasing interest to mitigate environmental risks. The development of Electrical Resistivity Tomography (ERT) monitoring station systems has triggered the application of Time-Lapse ERT technique to detect the resistivity variations in time. Indeed, the resistivity change can be associated with subsurface processes such as groundwater recharge (Descloitres et al., 2018) and aquifer contamination (Maurya et al., 2017). The ERT data inversion is an ill-posed, non-linear problem that it is usually solved through a deterministic least-square algorithms. Nevertheless, this approach is prone to get stuck in local minima of the error function. A strategy to tackle this issue is to apply stochastic inversion algorithms that consider the model parameters as random variables and the subsurface model as a realisation of a probability density function. However, the main drawback of this approach is the computational burden related to the several forward model evaluations needed to sample the parameter space (Vinciguerra et al., 2022). The Ensemble Based (EB) algorithm is an iterative data assimilation method that assimilates the observed data multiple times with an inflated covariance matrix (Aleardi et al., 2021). The result of this algorithm is an ensemble of realisations from which an approximation of the posterior probability density function (ppd) can be numerically assessed. This algorithm requires less computational time compared to standard Monte Carlo Markov Chain algorithms due to the lower number of forward modelling runs to estimate the ppd. (Aleardi et al., 2021). Nowadays, there exists different Time-Lapse inversion strategies: independent approach in which the data are inverted independently; the cascaded inversion, in which the inversion result of the first dataset is considered as starting model of the second dataset (Miller et al., 2008); or strategies such as the difference inversion that attenuate the effects of the systematic error during the acquisition (Labreque et al., 2001). To perform a Time-Lapse inversion, we rearrange the EB algorithm to obtain the posterior mean model, its variation in time and the uncertainties affecting the solution. In this work, we apply the Time-Lapse EB algorithm to field data acquired by a landfill monitoring station in Pillemark (Samsø, Denmark).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
