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Updated: Jun 28, 2026
09:01
Using Solution NMR to Characterize Biomolecular Condensates Under Biphasic Conditions
Published on: April 17, 2026
Quantifying uncertainty in NMR T2 spectra using Monte Carlo inversion.
1Schlumberger-Doll Research, One Hampshire Street, Cambridge, MA 02139, USA. prange@slb.com
Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|October 28, 2008
Summary
This study introduces a Monte Carlo algorithm for analyzing relaxation and diffusion data. This method generates numerous probable solutions, offering a novel way to analyze statistical properties and quantify uncertainties in derived parameters like porosity.
Area of Science:
- Geophysics
- Materials Science
- Physical Chemistry
Background:
- Laplace inversion is commonly used for relaxation and diffusion data analysis.
- Regularization is essential due to the ill-conditioned nature of Laplace inversion with noisy, finite data.
- Existing methods often rely on ad hoc criteria to select a single 'best' solution.
Purpose of the Study:
- To develop an efficient Monte Carlo algorithm for Laplace inversion.
- To analyze the statistical properties of multiple probable solutions.
- To provide a method for characterizing the uncertainty of derived quantities.
Main Methods:
- Developed an efficient Monte Carlo algorithm for Laplace inversion.
- Generated thousands of probable spectral solutions.
- Analyzed statistical properties of the ensemble of solutions.
- Derived probability distributions for porosity and bound fluid.
Main Results:
- Individual Monte Carlo solutions are spiky.
- The mean solution spectrum is smooth and comparable to regularized solutions.
- Probability distributions for porosity and bound fluid were successfully obtained.
Conclusions:
- The Monte Carlo approach provides a robust alternative to traditional regularization for Laplace inversion.
- This method allows for the quantification of uncertainty in parameters derived from spectral analysis.
- Characterizing uncertainty in porosity and bound fluid is a significant advancement.
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