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Assessing the robustness of Bel1D for inverting TEM data

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Abstract
Subsurface is of prime importance for many geological and hydrogeological applications. Geophysical methods offer an economical alternative for investigating the subsurface compared to costly boreholes investigation methods. Geophysics provides a wide range of approaches that can models of the subsurface, traditionally by inversion process. Basically, there are two types of the inversion deterministic and stochastic inversion. The difference between them is the extent of uncertainty in their results. Deterministic inversion is very certain which have no ability to generate any uncertainty, on the other hand stochastic inversion are often very expensive. In this research Firstly, we tried to find out the effect of time and space discretization on the posterior models or on the uncertainty quantification of models generated in BEL1D. Secondly, we discussed the importance of prior selection and thirdly, we tried to quantify the salinity of the TDEM data taken in Vietnam south central province which have been facing saltwater intrusions problem for many years particularly in Binah Thuan province) by combining a new stochastic approach called Bayesian evidential learning 1D imaging (BEL1D) with SimPEG (an open-source python package for solving the electromagnetic forward and inverse problem) as a forward solver. BEL1D bypasses the inversion step by generating random samples from the prior model distribution (with predefined ranges for thickness, electrical conductivity, and salinity for the different layers). It then directly generates the corresponding data to learn a direct statistical relationship between data and model parameters. From this relationship, BEL1D can generate posterior models fitting the field observed data, without additional forward model computations, making it a very efficient way to stochastically solve the inverse problem. The output of BEL1D shows the range of uncertainty for subsurface models. It enables to identify which model parameters are sensitive and can thus be accurately estimated from TDEM data. In our case, it reveals the uncertainty on the depth of fresh saline interface as well as the total dissolved solid content of groundwater. The application of BELID together with SimPEG for stochastic TDEM inversion is a very efficient approach as it allows to estimate the uncertainty at a limited cost. We thus expect our approach to be also valuable for the inversion of airborne data sets.
Keywords
Saltwater intrusion, groundwater, uncertainty, TDEM, BEL1D, SimPEG

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MLA
Ahmed, Arsalan, et al. “Assessing the Robustness of Bel1D for Inverting TEM Data.” NSG2022 28th European Meeting of Environmental and Engineering Geophysics, vol. 2022, 2022, pp. 1–5, doi:10.3997/2214-4609.202220101.
APA
Ahmed, A., Aignar, L., Deleersnyder, W., Michel, H., Flores-Orozco, A., Dudal, D., & Hermans, T. (2022). Assessing the robustness of Bel1D for inverting TEM data. NSG2022 28th European Meeting of Environmental and Engineering Geophysics, 2022, 1–5. https://doi.org/10.3997/2214-4609.202220101
Chicago author-date
Ahmed, Arsalan, L. Aignar, Wouter Deleersnyder, Hadrien Michel, A. Flores-Orozco, D. Dudal, and Thomas Hermans. 2022. “Assessing the Robustness of Bel1D for Inverting TEM Data.” In NSG2022 28th European Meeting of Environmental and Engineering Geophysics, 2022:1–5. https://doi.org/10.3997/2214-4609.202220101.
Chicago author-date (all authors)
Ahmed, Arsalan, L. Aignar, Wouter Deleersnyder, Hadrien Michel, A. Flores-Orozco, D. Dudal, and Thomas Hermans. 2022. “Assessing the Robustness of Bel1D for Inverting TEM Data.” In NSG2022 28th European Meeting of Environmental and Engineering Geophysics, 2022:1–5. doi:10.3997/2214-4609.202220101.
Vancouver
1.
Ahmed A, Aignar L, Deleersnyder W, Michel H, Flores-Orozco A, Dudal D, et al. Assessing the robustness of Bel1D for inverting TEM data. In: NSG2022 28th European Meeting of Environmental and Engineering Geophysics. 2022. p. 1–5.
IEEE
[1]
A. Ahmed et al., “Assessing the robustness of Bel1D for inverting TEM data,” in NSG2022 28th European Meeting of Environmental and Engineering Geophysics, Belgrade, Serbia, 2022, vol. 2022, pp. 1–5.
@inproceedings{01GK70A8TPAXP0GW1KP58BF9KN,
  abstract     = {{Subsurface is of prime importance for many geological and hydrogeological applications. 
Geophysical methods offer an economical alternative for investigating the subsurface 
compared to costly boreholes investigation methods. Geophysics provides a wide range of 
approaches that can models of the subsurface, traditionally by inversion process. Basically,
there are two types of the inversion deterministic and stochastic inversion. The difference 
between them is the extent of uncertainty in their results. Deterministic inversion is very 
certain which have no ability to generate any uncertainty, on the other hand stochastic 
inversion are often very expensive. In this research Firstly, we tried to find out the effect of 
time and space discretization on the posterior models or on the uncertainty quantification of 
models generated in BEL1D. Secondly, we discussed the importance of prior selection and 
thirdly, we tried to quantify the salinity of the TDEM data taken in Vietnam south central 
province which have been facing saltwater intrusions problem for many years particularly in 
Binah Thuan province) by combining a new stochastic approach called Bayesian evidential 
learning 1D imaging (BEL1D) with SimPEG (an open-source python package for solving the 
electromagnetic forward and inverse problem) as a forward solver. BEL1D bypasses the 
inversion step by generating random samples from the prior model distribution (with 
predefined ranges for thickness, electrical conductivity, and salinity for the different layers). It 
then directly generates the corresponding data to learn a direct statistical relationship 
between data and model parameters. From this relationship, BEL1D can generate posterior 
models fitting the field observed data, without additional forward model computations, 
making it a very efficient way to stochastically solve the inverse problem. The output of BEL1D 
shows the range of uncertainty for subsurface models. It enables to identify which model 
parameters are sensitive and can thus be accurately estimated from TDEM data. In our case, 
it reveals the uncertainty on the depth of fresh saline interface as well as the total dissolved 
solid content of groundwater. The application of BELID together with SimPEG for stochastic 
TDEM inversion is a very efficient approach as it allows to estimate the uncertainty at a limited 
cost. We thus expect our approach to be also valuable for the inversion of airborne data sets.

}},
  author       = {{Ahmed, Arsalan and Aignar, L. and Deleersnyder, Wouter and Michel, Hadrien and Flores-Orozco, A. and Dudal, D. and Hermans, Thomas}},
  booktitle    = {{NSG2022 28th European Meeting of Environmental and Engineering Geophysics}},
  keywords     = {{Saltwater intrusion,groundwater,uncertainty,TDEM,BEL1D,SimPEG}},
  language     = {{eng}},
  location     = {{Belgrade, Serbia}},
  pages        = {{1--5}},
  title        = {{Assessing the robustness of Bel1D for inverting TEM data}},
  url          = {{http://doi.org/10.3997/2214-4609.202220101}},
  volume       = {{2022}},
  year         = {{2022}},
}

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