A Bayesian method to quantifying chemical composition using NMR: Application to porous media systems

This paper describes a Bayesian approach for inferring the chemical composition of liquids in porous media obtained using nuclear magnetic resonance (NMR). The model analyzes NMR data automatically in the time domain, eliminating the operator dependence of a conventional spectroscopy approach. The technique is demonstrated and validated experimentally on both pure liquids and liquids imbibed in porous media systems, which are of significant interest in heterogeneous catalysis research. We discuss the challenges and practical solutions of parameter estimation in both systems. The proposed Bayesian NMR approach is shown to be more accurate and robust than a conventional spectroscopy approach, particularly for signals with a low signal-to-noise ratio (SNR) and a short life time.