![]() We demonstrate the capacity of this model through a series of tests on synthetic data sets and a published empirical data set from North America mixed in known proportions this proof-of-concept testing shows the model is capable of accurately unmixing highly complex distributions. ![]() Quantitative comparison is based on the Kolmogorov-Smirnov (KS) test D statistic and Kuiper test V statistic for cumulative distribution functions, and the Cross-correlation coefficient for finite mixture distributions (probability density plots or kernel density estimates). Results may then be used to constrain a forward optimization routine to find a single best-fit mixture. We developed a mixing model that determines mixing proportions for source samples through inverse Monte Carlo modeling, wherein mixed samples are compared to randomly generated combinations of source distributions, and a range of best mixing proportions are retained. Despite recent advances in quantitative methods of detrital provenance analysis, there is currently no widely accepted method of unmixing detrital geochronology data.
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