Central ages, mono-kinetic, or multi-kinetic? Assessing detrital AFT thermochronology in a Cretaceous foreland basin
Notice bibliographique
Résumé
Apatite fission track (AFT) thermochronology can resolve thermal histories over a temperature window that spans 50–150°C. Herein, we present a case study from the Peel Plateau, a foreland basin of the Mackenzie Mountains, northern Canadian Cordillera, where we compare three interpretations from a single AFT sample: 1) the central age, and thermal history modelling of 2) a mono-kinetic population and 3) a multi-kinetic population. The AFT sample is derived from a lithic wacke with an Albian depositional age and yields an AFT central age of 67 ± 9 Ma (n: 39). Despite central ages often being interpreted to reflect a sample’s 'cooling age,' laboratory experiments have demonstrated that fission tracks anneal over a wide temperature range, corresponding to the partial annealing zone (PAZ). AFT ages from samples that have undergone multiple burial and exhumation events likely represent partial annealing due to their protracted residence in the PAZ and require thermal history modelling to derive a meaningful geologic interpretation. It is unlikely the Peel Plateau experienced a simple thermal history with rapid cooling through the PAZ, as a regional unconformity separates the Albian and Cenomanian strata across much of the region. Thus, the central age does not necessarily reflect a meaningful thermal event. Thermal modelling the data as a mono-kinetic population predict peak burial temperatures of 118–166°C at 65–92 Ma, however, the sample fails the χ2, indicating it does not comprise a single statistical age population. Intra-sample age dispersion is often indicative of samples that comprise multi-kinetic population and grain specific chemistry is known to strongly influence apatite’s annealing kinetics. Most notably apatite with high F content will undergo thermal annealing at lower temperatures than apatite with high Cl content, although numerous other elements (e.g. OH, Mn, Fe) are known to effect annealing kinetics. The rmro parameter was developed through laboratory annealing experiments, and accounts for compositional controls on apatite’s kinetic behaviour. Apatite grain chemistry was measured via EMPA methods to calculate rmro values and used to separate AFT samples into two kinetic populations (resulting in pooled ages of 36 ± 5 Ma and 103 ± 12 Ma) that both pass the χ2 test. Pooled ages incorporate information of the grains’ pre-depositional history and U-Pb dating can serve as an additional tool to decipher between different kinetic populations. These populations then act as independent thermochronometers capable of resolving different temperature windows. Compared to thermal history model results from mono-kinetic data, the multi-kinetic model predicts significantly lower burial temperatures of 83–93°C, which may have occurred over a longer duration (33–88 Ma). The central age for this sample overlaps with the timing of peak burial predicted by the multi-kinetic model, therefore does not inform us about the cooling history. Ultimately, the purpose of this study is to highlight the importance of assessing multi-kinetic behaviour in sedimentary samples, as these interpretations provide statistically significant age populations and robust thermal history models. Thermal history models that ignore multi-kinetic behaviour may lead to erroneous geologic interpretations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».