TY - JOUR
T1 - Stochastic inversion of time-lapse electrical resistivity tomography data by means of an adaptive ensemble-based approach
AU - Vinciguerra, Alessandro
AU - Aleardi, Mattia
AU - Madsen, Line Meldgaard
AU - Bording, Thue Sylvester
AU - Christiansen, Anders Vest
AU - Stucchi, Eusebio
N1 - Publisher Copyright:
© 2023 The Authors. Geophysical Prospecting published by John Wiley & Sons Ltd on behalf of European Association of Geoscientists & Engineers.
PY - 2024/1
Y1 - 2024/1
N2 - Inversion of time-lapse electrical resistivity tomography is an extension of the conventional electrical resistivity tomography inversion that aims to reconstruct resistivity variations in time. This method is widely used in monitoring subsurface processes such as groundwater evolution. The inverse problem is usually solved through deterministic algorithms, which usually guarantee a fast solution convergence. However, the electrical resistivity tomography inverse problem is ill-posed and non-linear, and it could exist more than one resistivity model that explains the observed data. This paper explores a Bayesian approach based on data assimilation, the ensemble smoother multiple data assimilation. In particular, we apply an adaptive approach in which the inflation coefficient is chosen based on the error function, that is the ensemble smoother multiple data assimilation restricted step. Our inversion approach aims to invert the data acquired at two different times simultaneously, estimating the resistivity model and its variation. In addition, the Bayesian approach allows for the assessment of the posterior probability density function needed for quantifying the uncertainties associated with the results. To test the method, we first apply the algorithm to synthetic data generated from realistic resistivity models; then, we invert field data from the Pillemark landfill monitoring station (Samsø, Denmark). Inversion results show that the ensemble smoother multiple data assimilation restricted step can correctly detect the resistivity variation both in the synthetic and in the field case, with an affordable computational burden. In addition, assessing the uncertainties allows us to interpret the reconstructed resistivity model correctly. This paper demonstrates the potential of the data assimilation approach in Bayesian time-lapse electrical resistivity tomography inversion.
AB - Inversion of time-lapse electrical resistivity tomography is an extension of the conventional electrical resistivity tomography inversion that aims to reconstruct resistivity variations in time. This method is widely used in monitoring subsurface processes such as groundwater evolution. The inverse problem is usually solved through deterministic algorithms, which usually guarantee a fast solution convergence. However, the electrical resistivity tomography inverse problem is ill-posed and non-linear, and it could exist more than one resistivity model that explains the observed data. This paper explores a Bayesian approach based on data assimilation, the ensemble smoother multiple data assimilation. In particular, we apply an adaptive approach in which the inflation coefficient is chosen based on the error function, that is the ensemble smoother multiple data assimilation restricted step. Our inversion approach aims to invert the data acquired at two different times simultaneously, estimating the resistivity model and its variation. In addition, the Bayesian approach allows for the assessment of the posterior probability density function needed for quantifying the uncertainties associated with the results. To test the method, we first apply the algorithm to synthetic data generated from realistic resistivity models; then, we invert field data from the Pillemark landfill monitoring station (Samsø, Denmark). Inversion results show that the ensemble smoother multiple data assimilation restricted step can correctly detect the resistivity variation both in the synthetic and in the field case, with an affordable computational burden. In addition, assessing the uncertainties allows us to interpret the reconstructed resistivity model correctly. This paper demonstrates the potential of the data assimilation approach in Bayesian time-lapse electrical resistivity tomography inversion.
KW - inversion
KW - resistivity
KW - time lapse
UR - http://www.scopus.com/inward/record.url?scp=85179706532&partnerID=8YFLogxK
U2 - 10.1111/1365-2478.13464
DO - 10.1111/1365-2478.13464
M3 - Journal article
AN - SCOPUS:85179706532
SN - 0016-8025
VL - 72
SP - 268
EP - 284
JO - Geophysical Prospecting
JF - Geophysical Prospecting
IS - 1
ER -