首页|4D inversion of resistivity monitoring data with adaptive time domain regularization
4D inversion of resistivity monitoring data with adaptive time domain regularization
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NSTL
Elsevier
The time-lapse or 4D inversion aims to image significant underground changes and efficiently suppress inversion artefacts. However, the inverted model changes are frequently over-smoothed along the time-axis. Particularly in cases where models have poor resolution, even significant model changes are severely blurred or blunted in the reconstructed image. In this study, we developed a new 4D inversion algorithm that automatically determines time domain (TD) regularization based on model changes and the resolution to achieve a more accurate interpretation of resistivity monitoring data. By assigning a smaller TD constraint to model parameters with poor resolution, model changes at depth can be imaged if the models are significantly changed over time. Moreover, TD regularization was adaptively adjusted according to the TD error roughness, which can effectively control model smoothness. We conducted inversion experiments on synthetic and field data to test the performance of the proposed inversion algorithm. The inversion results for synthetic data demonstrated that the 4D inversion with adaptive TD regularization yields the model change that is most similar to the true change, among various options of TD regularization. The adaptive 4D inversion confirmed its capability to clearly image widespread and locally confined model changes. Model changes with poor resolution could likewise be imaged. Finally, the applicability of the proposed 4D inversion algorithm was demonstrated with real resistivity monitoring data collected for leakage detection at an embankment dam in Korea.
NSTL
Elsevier
