Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

Abstract

Faced with ongoing depletion of near-surface ore deposits, geologists are increasingly required to explore for deep deposits or those lying beneath surface cover. The result is increased drilling costs and a need to maximize the value of the drill hole samples collected. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite is one tool that is showing promise in deep exploration. Since the trace element content of pyrite approximates the composition of the fluid from which it precipitated and the crystallization mechanism, the trace element characteristics can be used to predict the type of deposit with which a pyritic sample is associated. This possibility, however, is complicated by overlapping trace element abundances for many deposit types. The solution lies with simultaneous comparison of multiple trace elements through rigorous statistical analysis. Specifically, we used LA-ICP-MS pyrite trace element data and Random Forests, an ensemble machine learning supervised classifier, to distinguish barren sedimentary pyrite and five ore deposit categories: iron oxide copper-gold (IOCG), orogenic Au, porphyry Cu, sedimentary exhalative (SEDEX), and volcanic-hosted massive sulfide (VHMS) deposits. The preferred classifier utilizes in situ Co, Ni, Cu, Zn, As, Mo, Ag, Sb, Te, Tl, and Pb measurements to train the Random Forests. Testing of the Random Forests classifier using additional data from the same deposits and sedimentary basins (test data set) yielded an overall accuracy of 91.4% (94.9% for IOCG, 78.8% for orogenic Au, 81.1% for porphyry Cu, 93.6% for SEDEX, 97.2% for sedimentary pyrite, 91.8% for VHMS). Similarly, testing of the Random Forests classifier using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0% (81.4% for orogenic Au, 95.5% for SEDEX, 90.0% for sedimentary pyrite, 73.9% for VHMS; insufficient data was available to perform a blind test on porphyry Cu and IOCG). The performance of the classifier was further improved by instituting criteria (at least 40% of total votes from the Random Forests needed for a conclusive identification) to remove uncertain or inconclusive classifications, increasing the classifier's accuracy to 94.5% for the test data (94.6% for IOCG, 85.8% for orogenic Au, 87.8% for porphyry Cu, 95.4% for SEDEX, 98.5% for sedimentary pyrite, 94.6% for VHMS) and 93.9% for the blind test data (85.5% for orogenic Au, 96.9% for SEDEX, 96.7% for sedimentary pyrite, 84.6% for VHMS). The Random Forests classification models for pyrite trace element data can be used as a predictive modeling tool in greenfield terrains by providing an accurate indication of ore deposit type. This advance will assist mineral explorers by allowing early implementation of predictive ore deposit models when prospecting for ore deposits. Furthermore, the ability of the classifier to accurately identify pyrite of sedimentary origin will allow researchers interested in paleoenvironmental conditions of ...

Authigenesis at the Urals Massive Sulfide Deposits: Insight from Pyrite Nodules Hosted in Ore Diagenites

“…These fractures also host authigenic chalcopyrite and fahlores in the Saf'yanovskoe deposit and sphalerite, chalcopyrite, and galena in the Talgan deposit, which also replace the core of the nodules. According to these morphological data and the growth models of pyrite nodules recently suggested by [58], our nodules formed pervasively around numerous nucleation centers inside the background sediments with minor involvement of seawater and their formation results in diagnostic trace element zonation. Nodules formed in this fashion also have relatively more matrix inclusions [58], which is also supported by our case.…”

Multivariate Statistical Analysis of Trace Elements in Pyrite: Prediction, Bias and Artefacts in Defining Mineral Signatures

“…Published data for trace elements in pyrite from IOCG systems are relatively limited despite the fact that it is abundant and a potential host for metals of importance (e.g., [34][35][36]). There are also only few published examples of multivariate statistical analyses applied to pyrite data (e.g., [37,38]), although the potential application of multivariate statistical methods to issues of acid mine drainage has been recently recognised [39].…”

Ground-truthing the pyrite trace element proxy in modern euxinic settings

“…Median error estimates (1 σ) for the analyses are in table 4. Pyrite trace element content is rarely, if ever, normally data distributed so summarizing the data as a mean and standard deviation is inappropriate (Gregory, et al 2019(Gregory, et al , 2020. While in some cases pyrite trace element abundance is lognormal so geometric means and multiplicative standard deviations can be used our data set is too small to be confident that the distribution is lognormal ) so instead we summarize the data using the median and median absolute deviation.…”