Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis
Methodology
2026-01-05 v1 Optimization and Control
Abstract
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.
Cite
@article{arxiv.2601.00154,
title = {Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis},
author = {Qianqian Qi and Zhongming Chen and Peter G. M. van der Heijden},
journal= {arXiv preprint arXiv:2601.00154},
year = {2026}
}