1887

Abstract

Summary

Standard inversion of time-lapse geophysical suffers from spatially and temporally varying resolution due to the regularization procedure used during the inversion process. In this study, we apply the recently developed prediction-focused approach (PFA) to directly estimate temperature with electrical resistance data, without classic tomographic inversions. PFA is based on a set of prior subsurface models coherent with our prior knowledge of the site. From this set of models, we generate a prior set of temperature distribution and resistance data mimicking the field experiment. Then, we use dimension-reduction techniques to derive a direct relationship between the data and the desired prediction. The use of canonical correlation analysis linearize the relationship and allows using Gaussian regression to sample the posterior. In this paper, we demonstrate the ability of PFA to process time-lapse ERT data during a field experiment. We propose an analysis of time-lapse reciprocals to derive an error model and generate the posterior distribution of temperature. We validate the results using direct measurements in the aquifer. This successful application opens new ways to process and integrate geophysical data in hydrogeological model.

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/content/papers/10.3997/2214-4609.201702086
2017-09-03
2020-09-21
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References

  1. Hermans, T., Kemna, A., & Nguyen, F.
    [2016a] Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data. Geophysics, 81(5), E311–E322.
    [Google Scholar]
  2. Hermans, T., Oware, E.K., & Caers, J.
    [2016b]. Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data.Water Resources Research, 52, 7262–7283.
    [Google Scholar]
  3. Hermans, T., Wildemeersch, S., Jamin, P., Orban, P., Brouyère, S., Dassargues, A., & Nguyen, F.
    [2015]. Quantitative temperature monitoring of a heat tracing experiment using cross-borehole ERT. Geothermics, 53, 14–26.
    [Google Scholar]
  4. Irving, J., & Singha, K.
    [2010]. Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities. Water Resources Research, 46, W11514.
    [Google Scholar]
  5. Nguyen, F., Kemna, A., Robert, T. & Hermans, T.
    [2016]. Data-driven selection of the minimumgradient support parameter in time-lapse focused electric imaging. Geophysics, 81(1), A1–A5.
    [Google Scholar]
  6. Oware, E. K., Moysey, S. M., & Khan, T.
    [2013], Physically based regularization of hydrogeophysical inverse problems for improved imaging of process-driven systems. Water Resources Research, 49, 6238–6247.
    [Google Scholar]
  7. Singha, K., Day-Lewis, F. D., Johnson, T. & Slater, L.
    [2015]. Advances in interpretation of subsurface processes with time-lapse electrical imagin. Hydrological Processes, 29(6), 1549–1576.
    [Google Scholar]
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