Myers, Michael T.2019-09-182019-09-18August 2012017-08August 201https://hdl.handle.net/10657/48361-D NMR data inversion is the process of obtaining T2 amplitude distribution from NMR spin echo trains. NMR inversion is an ill-posed problem, the noise in the data allows for many possible solutions. We solve for the amplitudes on an equally spaced logarithmic scale, representing a typical pore system distribution. If a least square method is used the solution is highly unstable, so we adopted a technique, Tikhonov-Regularization to restrict the range of possible solutions. A stable version of this algorithm was developed and a detailed parametric studies was performed. Two different L-cure parametrizations, the slope of the L-curve method and adaptive pruning was adopted. We generated forward models for the T2 echo train, using an assumption of multiply peaked amplitude distributions with varying levels of Gaussian noise. The forward models were then inverted using the developed algorithm to examine how much of the original information was lost. We conclude with a discussion of the limitations of NMR inversion and the relative merits of each L-curve parametrization.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).NMRNMR InversionL-CurveIll-Posed2019-09-18Thesisborn digital