Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale.
Understanding magma plumbing systems hinges upon an intricate comprehension of crystal populations concerning size, chemistry, and origin. We introduce an innovative, yet elegantly simple approach—the ‘number–length of crystals (N-LoC) multifractal model’—to classify crystal sizes, unveiling compelling insights into their distribution dynamics. This model, a departure from conventional crystal size distribution (CSD) diagrams, reveals multifractal patterns indicative of distinct class sizes within igneous rock crystals. By synthesizing multiple samples from experimental studies, natural occurrences, and numerical models, we validate this method’s efficacy. Our bi-logarithmic N-LoC diagrams for cooling-driven crystallized samples transcend the confines of traditional CSD plots, identifying variable thresholds linked to cooling rates and quenching temperatures. These thresholds hint at pulsative nucleation and size-dependent growth events, offering glimpses into crystallization regimes and post-growth modifications like coalescence and coarsening. Examining multifractal log–log plots across time-series samples unravels crystallization histories during cooling or decompression. Notably, microlites within volcano conduits delineate thresholds influenced by decompression rate and style, mirroring nucleation and growth dynamics observed in experimental studies. Our fractal methodology, presenting a more direct approach with fewer assumptions than the classic CSD method, stands poised as a potent alternative or complementary tool. We delve into its potential, facilitating comparisons between eruptive styles in volcanoes while deliberating on inherent limitations. This work not only advances crystal size analysis methodologies but also holds promise for inferring nuanced volcanic processes and offers a streamlined avenue for crystal size evaluation in igneous rocks.
Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.
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