Predictive Models for Detrital Titanite Provenance With Application to the Nanga Parbat—Haramosh Syntaxial Massif, Western Himalaya

Abstract

AbstractTitanite is a versatile recorder of crystallization age, temperature, and host lithology, via the U–Pb system, the Zr‐in‐Ttn thermometer, and elemental composition, respectively. The paragenesis of titanite renders it especially useful for tracing detritus derived from lithologies that are infertile for the commonly used detrital zircon U‐Pb chronometer, such as sub‐anatectic metamorphism of calc‐silicates. Despite these advantages, detrital titanite analysis is underemployed, in part because the U–Pb system in titanite is often complicated by the incorporation of both inherited radiogenic Pb from precursor minerals during metamorphic reactions, and also bulk crustal common‐Pb. Recent systematic analyses of large titanite compositional data sets from diverse source rocks have revealed that the elemental composition of titanite is provenance‐specific. Here, we apply a workflow that incorporates a machine‐learning classifier to a large and representative compositional database for titanite, encompassing >11,000 analyses, with c. 6,700 points passed to our model. Only medians of the subcompositions for 205 rocks are used for our model. We reliably discriminate (>90%) between metamorphic and igneous titanite. Application of this classifier to a detrital case study from the Nanga Parbat‐Haramosh syntaxial massif of the western Himalaya reveals that titanite of different compositions formed during different orogenic events. Furthermore, titanite with significant common Pb solely derives from medium/low grade metasedimentary rocks. The method described here offers a pathway to increase the specificity of the provenance information derived from titanite; however, the published corpus of titanite data will have to be much larger before multi‐class source‐rock discrimination can be achieved reliably.