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| Titel |
Coupled vs. uncoupled hydrogeophysical inversion via ensemble Kalman filter assimilation of ERT-monitored tracer test data |
| VerfasserIn |
Matteo Camporese, Andrew Binley, Giorgio Cassiani, Rita Deiana, Paolo Salandin |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250077876
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| Zusammenfassung |
| Recent advances in geophysical methods have been increasingly exploited as inverse
modeling tools in groundwater hydrology. In particular, several attempts to constrain the
hydrogeophysical inverse problem to reduce inversion error have been made using time-lapse
geophysical measurements through both coupled and uncoupled inversion approaches.
The main advantage of coupled approaches is that the numerical models for the
geophysical and hydrological processes are linked together such that the geophysical data
are inverted directly for the hydrological properties of interest. On the other hand,
uncoupled approaches allow assessing in advance the reliability of the data, thanks to the
geophysical inversion that is carried out before estimating the hydrological variable of
interest. In spite of the recent popularity of fully coupled inversion approaches, we
argue that their superiority over uncoupled methods still needs to be proven. The
objective of this work is to shed some light on this debate. An approach based on the
Lagrangian formulation of transport and the ensemble Kalman filter (EnKF) is here
applied to assess the spatial distribution of hydraulic conductivity (K) by assimilating
ERT data generated for a synthetic tracer test in a heterogeneous aquifer. In the
coupled version of our inverse modeling tool, the K distribution is retrieved by
assimilating raw ERT voltage data without the need for a preliminary electrical inversion.
In the uncoupled version, K is estimated by assimilating time-lapse cross-hole
electrical resistivity tomography (ERT) images derived by an electrical inversion. We
compare the performance of the two approaches in a number of simulation scenarios
and assess the impact on the inversions of the choice of the prior statistics of K. |
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