Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908743843708928 |
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| author | Qi, Qianqian Chen, Zhongming van der Heijden, Peter G. M. |
| author_facet | Qi, Qianqian Chen, Zhongming van der Heijden, Peter G. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis Qi, Qianqian Chen, Zhongming van der Heijden, Peter G. M. Methodology Optimization and Control 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. |
| title | Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis |
| topic | Methodology Optimization and Control |
| url | https://arxiv.org/abs/2601.00154 |