Magnetic Resonance Tomography (MRT) is the only geophysical method where the measured signal is directly related to water distribution in the ground. This property allows three-dimensional imagery of water content by signal inversion routines. Because of its non linearity, the inverse problem has a quasi-infinite number of solutions implying as many possible spatial distributions of water content. A good answer to this problem, relevant for ice cavity detection and karstic structures mapping, is to provide a set of solutions consistent with the measured data. Markov Chain Monte Carlo (MCMC) algorithm applied to the MRT inverse problem provides a random exploration of the solutions giving the ability to compute probabilistic answer to a particular data set. For saturated structure detection, first results on synthetic cases demonstrate the routine ability to show an important anisotropy in the MRT resolution. Finally, a real case study, where the MCMC and linear inversion are compared, also shows the benefit of the method for a French Alp glacier cavity detection.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error