Endmember modelling of detrital zircon petrochronology data via multivariate Tucker-1 tensor decomposition
Notice bibliographique
Résumé
Detrital petrochronology is a powerful method of characterizing sediment and potentially sediment sources. The recently developed Tucker-1 decomposition method holds promise of using detrital petrochronology to identify both sediment-source characteristics and the proportions in which sources are present in sink samples even when sediment sources are unknown or unavailable for sampling. However, the correlation between endmember characteristics and lithological sources or proportions and sedimentary processes has not been established. Herein we present a case study of the recently developed Tucker-1 decomposition method to a multivariate geochemical data set from detrital zircons in till samples collected above the Cu-bearing Guichon Creek Batholith (GCB) in southern British Columbia, Canada. Data include a suite of eleven variables, including age, Ce anomaly, CeN/NdN, DyN/YbN, ΔFMQ, Eu anomaly, ΣHREE/ΣMREE, Hf, Th/U, Ti temperature, and YbN/GdN, from 12 samples from collected at a range of distances in the down ice-flow direction from the GCB.We demonstrate that endmember modelling using the Tucker-1 decomposition method successfully deconvolves the multivariate data sets into two endmembers in which the geochemical distributions are consistent with derivation from either non-oxidized and relatively anhydrous (i.e., low ore potential, Source 1) or oxidized and hydrous (i.e., potential ore bodies, Source 2) igneous rocks. Furthermore, we demonstrate that the proportions of the Source 2 endmember decrease with increasing distance from the ore bodies, as expected due to downstream zircon mixing and dilution.Finally, we attribute each of the zircon grains to either the Source 1 or 2 endmember based on maximization of the likelihood that their measured multivariate geochemistry was drawn from one or the other of the learned multivariate endmembers. We compared these grain attributions to the results of an independent Classification and Regression Tree (CART) analysis designed to characterize zircon grains as either “fertile” or “barren” with respect to copper based on their geochemistry. We find that there is ~80% overlap between the source attributions based on the CART analysis and the grain-source identification based on the Tucker-1 decomposition.We conclude that the novel Tucker-1 decomposition approach provides a flexible, precise, and accurate method of characterizing multivariate sediment sources even when those sources are unknown. It thus provides a basis for future petrochronological interpretations with applied and pure geoscience applications. All of the analyses presented herein can be freely accessed through a web application (https://dzgrainalyzer.eoas.ubc.ca/) or open-source Julia code (https://github.com/MPF-Optimization-Laboratory/MatrixTensorFactor.jl).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».