End member analysis (EMA) unmixes grain size distribution (GSD) data into a mixture of end members (EMs), thus helping understand sediment provenance and depositional regimes and processes. In highly mixed data sets, however, many EMA algorithms find EMs which are still a mixture of true EMs. To overcome this, we propose maximum volume constrained EMA (MVC-EMA), which finds EMs as different as possible. We provide a uniqueness theorem and a quadratic programming algorithm for MVC-EMA. Experimental results show that MVC-EMA can effectively find true EMs in highly mixed data sets.

翻译：端元分析（EMA）将粒度分布（GSD）数据解混为若干端元（EM）的混合物，从而有助于理解沉积物物源、沉积环境及沉积过程。然而，对于高度混合的数据集，许多EMA算法所识别的端元本身仍是真实端元的混合物。为解决此问题，我们提出最大体积约束端元分析（MVC-EMA），该方法旨在寻找差异尽可能大的端元。我们为MVC-EMA提供了唯一性定理及一种二次规划算法。实验结果表明，MVC-EMA能有效识别高度混合数据集中的真实端元。