Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis
Abstract End‐member mixing analysis (EMMA) is widely used to analyze geoscience data for their end‐members and mixing proportions. Many traditional EMMA methods depend on known end‐members, which are sometimes uncertain or unknown. Unsupervised EMMA methods infer end‐members from data, but many exis...
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000540 |
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| author | Zanchenling Wang Tao Wen |
| author_facet | Zanchenling Wang Tao Wen |
| author_sort | Zanchenling Wang |
| collection | DOAJ |
| description | Abstract End‐member mixing analysis (EMMA) is widely used to analyze geoscience data for their end‐members and mixing proportions. Many traditional EMMA methods depend on known end‐members, which are sometimes uncertain or unknown. Unsupervised EMMA methods infer end‐members from data, but many existing ones don't strictly follow necessary constraints and lack full mathematical interpretability. Here, we introduce a novel unsupervised machine learning method, simplex projected gradient descent‐archetypal analysis (SPGD‐AA), which uses the ML model archetypal analysis to infer end‐members intuitively and interpretably without prior knowledge. SPGD‐AA uses extreme corners in data as end‐members or “archetypes,” and represents data as mixtures of end‐members. This method is most suitable for linear (conservative) mixing problems when samples with similar characteristics to end‐members are present in data. Validation on synthetic and real data sets, including river chemistry, deep‐sea sediment elemental composition, and hyperspectral imaging, shows that SPGD‐AA effectively recovers end‐members consistent with domain expertise and outperforms conventional approaches. SPGD‐AA is applicable to a wide range of geoscience data sets and beyond. |
| format | Article |
| id | doaj-art-2cd8f9118e9b4d65b655fecc2fb3883e |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-2cd8f9118e9b4d65b655fecc2fb3883e2025-08-20T03:27:22ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2024JH000540Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal AnalysisZanchenling Wang0Tao Wen1Department of Earth and Environmental Sciences Syracuse University Syracuse NY USADepartment of Earth and Environmental Sciences Syracuse University Syracuse NY USAAbstract End‐member mixing analysis (EMMA) is widely used to analyze geoscience data for their end‐members and mixing proportions. Many traditional EMMA methods depend on known end‐members, which are sometimes uncertain or unknown. Unsupervised EMMA methods infer end‐members from data, but many existing ones don't strictly follow necessary constraints and lack full mathematical interpretability. Here, we introduce a novel unsupervised machine learning method, simplex projected gradient descent‐archetypal analysis (SPGD‐AA), which uses the ML model archetypal analysis to infer end‐members intuitively and interpretably without prior knowledge. SPGD‐AA uses extreme corners in data as end‐members or “archetypes,” and represents data as mixtures of end‐members. This method is most suitable for linear (conservative) mixing problems when samples with similar characteristics to end‐members are present in data. Validation on synthetic and real data sets, including river chemistry, deep‐sea sediment elemental composition, and hyperspectral imaging, shows that SPGD‐AA effectively recovers end‐members consistent with domain expertise and outperforms conventional approaches. SPGD‐AA is applicable to a wide range of geoscience data sets and beyond.https://doi.org/10.1029/2024JH000540end‐member mixing analysisarchetypal analysis |
| spellingShingle | Zanchenling Wang Tao Wen Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis Journal of Geophysical Research: Machine Learning and Computation end‐member mixing analysis archetypal analysis |
| title | Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis |
| title_full | Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis |
| title_fullStr | Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis |
| title_full_unstemmed | Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis |
| title_short | Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis |
| title_sort | inferring end members from geoscience data using simplex projected gradient descent archetypal analysis |
| topic | end‐member mixing analysis archetypal analysis |
| url | https://doi.org/10.1029/2024JH000540 |
| work_keys_str_mv | AT zanchenlingwang inferringendmembersfromgeosciencedatausingsimplexprojectedgradientdescentarchetypalanalysis AT taowen inferringendmembersfromgeosciencedatausingsimplexprojectedgradientdescentarchetypalanalysis |