Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis
Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis
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.
stat.ME, math.OC
Qi, Qianqian
47673ec0-7ef7-413d-8102-10789990f40c
Chen, Zhongming
b382f086-2965-4fc3-b58a-bf20c7ac81b0
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Qi, Qianqian
47673ec0-7ef7-413d-8102-10789990f40c
Chen, Zhongming
b382f086-2965-4fc3-b58a-bf20c7ac81b0
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
[Unknown type: UNSPECIFIED]
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.
Text
2601.00154v1
- Author's Original
Available under License Other.
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Accepted/In Press date: 1 January 2026
Keywords:
stat.ME, math.OC
Identifiers
Local EPrints ID: 509042
URI: http://eprints.soton.ac.uk/id/eprint/509042
PURE UUID: 2780e081-2479-409f-a658-fe546fa3b467
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Date deposited: 10 Feb 2026 17:48
Last modified: 11 Feb 2026 02:47
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Author:
Qianqian Qi
Author:
Zhongming Chen
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