Abstract :
[en] Apatite has been recognized as a robust tool for the study of magmatic volatiles in terrestrial and extraterrestrial systems due to its ability to incorporate various volatile components and its common occurrence in igneous rocks. Most previous studies have utilized apatite to study individual magmatic systems or regions. However, volatile systematics in terrestrial magmatic apatite formed under different geological environments has been poorly understood. In this study, we filtered a large compilation of data for apatite in terrestrial igneous rocks (n > 20,000), categorized the data according to tectonic settings, rock types, and bulk-rock compositions, and conducted statistical analyses of the F–Cl–OH–S–CO2 contents (~ 11,000 data for halogen and less for other volatiles). We find that apatite from volcanic arcs preserves a high Cl signature in comparison to other tectonic settings and the median Cl contents differ between arcs. Apatite in various types and compositions of igneous rocks shows overlapping F–Cl–OH compositions and features in some rock groups. Specifically, apatite in kimberlite is characterized as Cl-poor, whereas apatite in plutonic rocks can contain higher F and lower Cl contents than the volcanic counterparts. Calculation using existing partitioning models indicates that apatite with a high OH (or F) content does not necessarily indicate a H2O-rich (or H2O-poor) liquid because it could be a result of high (or low) magma temperature. Our work may provide a new perspective on the use of apatite to investigate volatile behavior in magma genesis and evolution across tectonic settings, volatile recycling at subduction zones, and the volcanic-plutonic connection.
Funding text :
We acknowledge Earth Observatory of Singapore, Asian School of the Environment, and the Facility for Analysis, Characterization, Testing and Simulation (FACTS) laboratory at Nanyang Technological University, and the Hawai\u2019i Institute of Geophysics and Planetology (HIGP) laboratory at University of Hawaii at Manoa for support on previous research that led to this work. W-R Li acknowledges Yishen Zhang for discussion and modification on the python module pyAp. The authors thank the editor Dante Canil for handling this manuscript, and Maryjo Brounce and an anonymous reviewer for their constructive reviews that helped to improve this manuscript. This work has been supported by the Faculty of Science, University of Hong Kong start-up grant to W-R Li.
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