The Origin and Discrimination of High-Ti Magnetite in Magmatic-Hydrothermal Systems: Insight from Machine Learning Analysis

Abstract

High-titanium (high-Ti, more than 1 wt % Ti) magnetite, commonly containing ilmenite exsolution, has long been attributed to an igneous origin and has been used as the most critical factor in previously developed discriminant diagrams. However, recent studies have shown that high-Ti magnetite can be present in high-temperature hydrothermal deposits, suggesting a probable hydrothermal origin. This also calls for reconsideration and necessary modification of the currently available discriminant diagrams. This high-Ti magnetite issue is particularly acute in iron oxide-apatite (IOA) deposits and raises controversy in the discussion of the origin of the high-Ti magnetite. With statistical analysis and machine learning techniques, this study applies two unsupervised dimensionality reduction methods—principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—on a compiled data set consisting of 876 laser ablation-inductively coupled plasma-mass spectrometry analyses of primary high-Ti magnetite from high-temperature ore-forming systems worldwide. Three models are built with different element combinations to identify magnetite of different origins. The models were further evaluated by the support vectors machine (SVM) and receiver operating characteristic (ROC) curves and proved to be able to describe the characteristics of trace element compositions of high-Ti magnetite of different origins. Our models suggest that Mg, Mn, Al, Ti, V, and Co from 59 analyzed trace elements show promising properties as effective discriminators, and on this basis, a new discrimination diagram of lg(Al) + lg(Ti) + lg(V) versus lg(Mn)/[lg(Co) + lg(Mg)] is developed for distinguishing high-Ti magnetite of igneous and hydrothermal origin. Our results also show that the high-Ti magnetite in the IOA deposits has chemical compositions similar to those of high-temperature hydrothermal deposits, including the iron oxide copper-gold and porphyry deposits, but significantly distinct from the igneous magnetite. Our study, hence, implies a magmatic-hydrothermal origin for the magnetite in IOA deposits.

The trace-element compositions of amphibole, magnetite and ilmenite as potential exploration guides to metamorphosed Proterozoic Cu–Zn±Pb±Au±Ag volcanogenic massive sulfide deposits in Colorado, USA

“…3g). However, recent studies of titanomagnetite by Hu et al (2022) show that high-Ti magnetite can be present in hydrothermal deposits and that exsolution between coexisting magnetite and ilmenite need not necessarily imply that they were the products of precipitation from a high-temperature magmatic fluid as was proposed previously by, for example, Knipping et al (2015) and La Cruz et al (2020).…”

Major elements geochemistry of chlorite in different ore deposits and its genesis and exploration significance: a case study from Naruo porphyry Cu deposit in Duolong ore district, Tibet

“…Principal Component Analysis (PCA) is a widely used statistical technique that transforms high-dimensional data into a new coordinate system by capturing the maximum variance in the first few principal components. This method was employed to simplify complex datasets and reveal the underlying patterns in the data (Jolliffe et al, 2002;Hu et al, 2022). PCA simplifies the data by replacing the original variables with a reduced set, preserving the maximum information through correlation, and explaining significant relationships in the first two principal components.…”

Application of Machine Learning to Research on Trace Elemental Characteristics of Metal Sulfides in Se-Te Bearing Deposits

“…Some researchers, such as Sun [21], Zhao [22], Zhong [23], and Dong [24], have applied different machine learning algorithms to identify ore genetic types of different Pb-Zn deposits using trace elemental data of sphalerite, which implies that trace elemental data of sphalerite can be applied to the classification of Pb-Zn deposits. In addition, Hu [25] used statistical analysis and machine learning techniques to analyze the collected dataset of primary high-Ti magnetite from the global high-temperature mineralization system and identify the ore genesis using principal component analysis (PCA) and t-distributed Stochastic Nearest Neighbor (t-SNE) mapping, then plotted a new discriminant diagram with an accuracy of 97%.…”