Zoeken
Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.
Boekenmand
Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).
RSS
Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.
nieuwe zoekopdracht
Machine learning classifiers for attributing tephra to source volcanoes: an evaluation of methods for Alaska tephras
Bolton, M.S.M.; Jensen, B.J.L.; Wallace, K.; Praet, N.; Fortin, D.; Kaufman, D.; De Batist, M. (2020). Machine learning classifiers for attributing tephra to source volcanoes: an evaluation of methods for Alaska tephras. J. Quaternary Sci. 35(1-2): 81-92. https://dx.doi.org/10.1002/jqs.3170
In: Journal of Quaternary Science. John Wiley & Sons: Harlow, Essex. ISSN 0267-8179; e-ISSN 1099-1417, meer
|   |
| Auteurs | | Top |
- Bolton, M.S.M.
- Jensen, B.J.L.
- Wallace, K.
- Praet, N.
|
- Fortin, D.
- Kaufman, D.
- De Batist, M.
|
|
| Abstract |
Glass composition‐based correlations of volcanic ash (tephra) traditionally rely on extensive manual plotting. Many previous statistical methods for testing correlations are limited by using geochemical means, masking diagnostic variability. We suggest that machine learning classifiers can expedite correlation, quickly narrowing the list of likely candidates using well‐trained models. Eruptives from Alaska's Aleutian Arc‐Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources. Artificial neural networks and random forests were consistently among the top‐performing learners (accuracy and kappa > 0.96). Their combination as an average ensemble effectively improves their performance. Using this combined model on tephras from Eklutna Lake, south‐central Alaska, showed that predictions match traditional methods and can speed correlation. Although classifiers are useful tools, they should aid expert analysis, not replace it. The Eklutna Lake tephras are mostly from Redoubt Volcano. Besides tephras from known Holocene‐active sources, Holocene tephra geochemically consistent with Pleistocene Emmons Lake Volcanic Center (Dawson tephra), but from a yet unknown source, is evident. These tephras are mostly anchored by a highly resolved varved chronology and represent new important regional stratigraphic markers. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
