New development in ERT time-lapse imaging published in Geophysics

Our unit is very active in geoelectrical imaging. In the last years, we focused on the development of improved imaging tools for time-lapse electrical resistivity tomography. Although the smoothness-constrained inversion is not always coherent with the (hydro)geological process under study, it is still the most applied technique to invert time-lapse data.

In collaboration with our colleague of Bonn University (Andreas Kemna), we developed two regularization functionals in the difference inversion code CRTomo.

The first functional is based on the minimum-gradient support (MGS) approach. The novelty in our approach is to use a data-driven approach to choose the optimum value of the MGS parameter. The methodology is demonstrated with a synthetic example and on a field salt tracer experiment in fractured limestone.

Nguyen, F., Kemna, A., Robert, T., & Hermans, T. (2016). Data-driven selection of the minimum-gradient support parameter in time-lapse focused electrical imaging. Geophysics, 81(1), A1-A5.

The second functional is based on parameter-difference covariance matrix (geostatistical constraint or CCI). Instead of imposing smooth parameter changes, the covariance matrix approach tends to recover a model with a defined correlation length. The latter can be estimated through independent measurements, borehole logs for example. The methodology is demonstrated with synthetic examples and on a field heat tracer experiment in an alluvial aquifer.

Hermans, T. , Kemna, A., Nguyen, F. (in press). Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data. Geophysics.

TL_inversion

As can be seen from the figure above, the different functionals are relatively similar qualitatively (two zones of reduced resistivity are observed) but yield relatively different quantitative results due to the assumption made on the resistivity change distribution (expressed in the regularization functionals). The MGS tends to produce homogeneous zone with sharp boundaries, whereas the two others produce smoother variations. In the geostatistical constraint, the smoothing is controlled by the imposed correlation length which tends to reduce the area affected by a decrease in resistivity. In this specific case, this is the solution the closest to the true distribution, as demonstrated by the comparison with direct measurements (figure below).

CCI

More can be found on similar developments for static inversion in previous articles published in Journal of Hydrology, Near Surface Geophysics, Waste Management and in T. Hermans’ thesis

 

BAEF Research Fellowship for Thomas Hermans at Stanford University

The Belgian American Educational Foundation (BAEF) has granted to Thomas Hermans a postdoctoral fellowship for research. The BAEF support student, scholar and scientist exchanges between Belgium and the United States.

Thomas will leave the applied geophysics unit for one year to pursue his research project at Stanford University between October 2015 and September 2016. He will work in collaboration with Jef Caers, Professor from the department of Geological Sciences at Stanford University and director from the Stanford Center for Reservoir Forecasting, on the integration of geophysical data in the inversion of thermo-hydrogeological model.

Fellows 2015 picture June 5, 2015

Here is the picture of the BAEF Fellows 2015 with the president of the Foundation Emile Boulpaep and Marie-Claude Hayoit who manages the Foundation in Belgium.

A new hydrogeophysical methodology to monitor shallow geothermal systems

The Postdoc project of Thomas Hermans is starting today. The aim of the project is to develop a new hydrogeophysical methodology to monitor shallow geothermal systems using electrical resistivity tomography.

It involves laboratory measurements to analyze the effect of temperature on electrical resistivity, including physico-chemical reactions such as carbonate precipitation. The project also includes field experiments such heat tracing, heat injection and storage or heat injection and recovery experiments.

To have more information on the use of geophysical methods to monitor temperature in the subsurface see this review or this paper.

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