A Machine‐Learning Classification for Torlesse Composite Terrane Petrofacies and its Application to Sediment Provenance

The Torlesse Composite Terrane (TCT) forms many of the mountain ranges in Aotearoa New Zealand and has provided enormous quantities of coarse‐grained sediment to Te Riu‐a‐Maui/Zealandia's basins since the mid‐Cretaceous. Tracing the provenance of these sediments to certain regions of the TCT can indirectly reconstruct exhumation patterns associated with the development of the Australian–Pacific plate boundary. Supplemented by new analyses, we are now able to include one of the new constituent terranes of the TCT (the Kaweka Terrane), which had not been recognised when the previous geochemical classification was developed. We train four different machine learning algorithms using samples from Te Waipounamu/the South Island to classify TCT‐derived sandstone conglomerate clasts into four different petrofacies/terranes of the TCT using log‐transformed trace element ratios. Balanced accuracies for the four algorithms used are 77% (linear support vector machine), 69% (k‐nearest neighbours), 75% (random forest), and 77% (neural network). This rises to 80% when an equal voting system is used. These accuracies are similar to existing classifications for the TCT but include the Kaweka Terrane as a classification option. We then show an example of how this updated classification can be applied to late Cenozoic TCT‐derived conglomerate clasts.