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Use of Groundwater Flux Data Measured by Active Fiber Optic DTS for Reconstruction of Aquifers Heterogeneity

Abstract : Subsurface characterization often relies on inversion of either pressure or tracer data. Unless data from many pumping and observation wells are available, the inversion process only resolves smooth low-resolution images of subsurface properties, which leads to less accurate subsurface flow and reactive transport predictions. Furthermore, tracer tomography can be very challenging and convergence to a global minimum is difficult. Active-distributed temperature sensing technology opens up the prospect of replacing tracer test data with estimates of subsurface groundwater flux. Here, the value of using estimated subsurface groundwater fluxes as a data source to reconstruct subsurface hydraulic properties is explored using a sequence of synthetic multivariate Gaussian aquifers with different measurement configurations. These results are compared to inversion of pressure data and joint inversion of the two data types with the inversions being based on the Principal Component Geostatistical Approach. Inversion of pressure data resulted in a smoothed reconstruction of aquifer heterogeneity capturing approximately high and low conductivity regions while ground water flux data inversion leads to higher-resolution estimates. To conclude, inversion of ground water flux whether individually or jointly with pressure data, can provide enhanced information about the heterogeneity of subsurface media compared with using pressure data alone.
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https://hal-insu.archives-ouvertes.fr/insu-02563346
Contributor : Isabelle Dubigeon <>
Submitted on : Tuesday, May 5, 2020 - 11:46:25 AM
Last modification on : Thursday, May 7, 2020 - 1:14:57 AM

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Behzad Pouladi Borj, Niklas Linde, Olivier Bour, Laurent Longuevergne. Use of Groundwater Flux Data Measured by Active Fiber Optic DTS for Reconstruction of Aquifers Heterogeneity. First EAGE Workshop on Fibre Optic Sensing, European Association of Geoscientists & Engineers, Mar 2020, Amsterdam, Netherlands. pp.1-5, ⟨10.3997/2214-4609.202030023⟩. ⟨insu-02563346⟩

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