### Speaker: Dr. Ofer Levi, Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

**Laplace inversion of LR-NMR relaxometry data using sparse representation methods**

LR-NMR relaxometry is a powerful tool that can be harnessed for characterizing constituents in complex materials. The technology is used for industrial quality control to measure solid-to-liquid and oil-to-water ratios in materials as diverse as oil-bearing rock, food emulsions, and plant seeds.

Conversion of the relaxation signal into a continuous distribution of relaxation components is an ill-posed problem. We provide a numerical optimization method for analyzing LR-NMR data by including L1 regularization and applying the convex optimization solver PDCO. Our integrated approach includes validation of analyses by simulations, testing repeatability of experiments, and validation of the model and its statistical assumptions. The method provides better resolved and more accurate solutions than those suggested by existing tools.