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Doctor2012 - 2016 (4 years)
Master2009 - 2011 (2 years)
VLF-EM Data Denoising and Reconstruction via Multivariate Empirical Mode Decomposition
In this paper we address two issues by employing the recently introduced noise assisted MEMD (N-A MEMD) for improving bivariate VLF-EM data. To demonstrate the robustness of the N-A MEMD method, the method was tested on added-noise synthetic data sets and the results were compared to the Ensemble EMD (EEMD) and Bivariate EMD (BEMD). The N-A MEMD gave more robust and accurate results than the EEMD and BEMD methods and the method required less CPU time to obtain the IMFs compared to EEMD.
The VLF-EM imaging of potential collapse on the LUSI embankment
VLF-EM profiles measured along the embankment crest provided an overview of the whole embankment and served to detect anomalous zones. In the selected area, the drill log and the standard penetration test (SPT) are valuable for supporting 2-D resistivity image obtained from VLF-EM data inversion. Furthermore, the low resistivity in embankment layer is associated to discontinuities (fracture, crack or fault) along the embankment which caused its collapse.
DREAM sampling method to provide model uncertainty of Rayleigh wave dispersion
In this paper, we applied and tested a Bayesian inversion method using a developed DREAM approach to provide PDMPs. Therefore, inverting Rayleigh wave dispersion using both approaches can produce the best model. We also applied these methods to Rayleigh wave dispersion data collected from the Ljubljana site in Slovenia. We compare our results with those estimated from the number of blow count in the standard penetrating test (N-SPT) data; it shows a good correlation toward each other.