Event: AGU Fall Meeting 2019, San Francisco (USA)
Poster by Behzad Pouladi, Niklas Linde, Olivier Bour, Laurent Longuevergne
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. This is reflected, for one of the considered examples, by a correlation coefficient that increases from 0.57 for the pressure data to 0.65 for the ground water flux data. The complimentary nature of the data sets is represented by a correlation coefficient that increases to 0.74 for the joint inversion of the two data types.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.