{"ID":2823356,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00154","arxiv_id":"2601.00154","title":"Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis","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.","short_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 propo...","url_abs":"https://arxiv.org/abs/2601.00154","url_pdf":"https://arxiv.org/pdf/2601.00154v1","authors":"[\"Qianqian Qi\",\"Zhongming Chen\",\"Peter G. M. van der Heijden\"]","published":"2026-01-01T01:05:10Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.OC\"]","methods":"[]","has_code":false}