Hengxu Li

China University of Geosciences (Beijing)

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Zhaochong Zhang

China University of Geosciences (Beijing)

M. Santosh

University of Adelaide

Zhiguo Cheng

China University of Geosciences (Beijing)

Bingxiang Liu

Hebei Mental Health Center

Ruixuan Zhang

Wuhan University

Linghan Liu

China University of Geosciences (Beijing)

Yilun Jin

China University of Geosciences (Beijing)

Qiuhong Xie

Chinese Academy of Geological Sciences

M. Santosh

China University of Geosciences (Beijing)

Marc Reichow

University of Leicester

Cooperative Institutions

China University of Geosciences (Beijing)

Chinese Academy of Geological Sciences

State Key Laboratory of Geological Processes and Mineral Resources

Chengdu University of Technology

Czech Academy of Sciences, Institute of Geology

Yonsei University

University of Adelaide

Kōchi University

China University of Geosciences

Chinese Academy of Sciences

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Ferrodoleritic dykes in the Tarim Craton signal Neoproterozoic breakup of Rodinia supercontinent

Journal of Asian Earth Sciences (2020)

Hengxu Li Zhaochong Zhang M. Santosh Zhenchao Wang Shengkai Jin

Rodinia

Supercontinent

10.1016/j.jseaes.2020.104476

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Citations (9)

Geochemical discrimination of pyrite in diverse ore deposit types through statistical analysis and machine learning techniques

American Mineralogist (2023)

Hengxu Li Zhaochong Zhang Ruixuan Zhang Qiuhong Xie Lei Zhang

Abstract Pyrite is a ubiquitous mineral in many ore deposits and sediments, and its trace element composition is mainly controlled by temperature, oxygen fugacity, pH, compositions of fluids, and host rock composition. A discriminant analysis (DA), based on multi-element compositions of pyrite, distinguishes iron oxide-apatite (IOA), iron oxide copper-gold (IOCG), skarn Cu-(Fe), porphyry Cu-Mo, orogenic Au, volcanic-hosted massive sulfide (VMS), sedimentary exhalative (SEDEX) deposits, and barren sedimentary pyrite. Testing of the DA classifier yields an accuracy of 98% for IOA, 96% for IOCG, 91% for skarn Cu-(Fe), 94% for porphyry Cu-Mo, 87% for orogenic Au, 84% for VMS, 96% for SEDEX, and 85% for barren sedimentary pyrite. Furthermore, neural network, support vector machine, and random forest, were performed for selecting the optimum classifier more accurately. In these three techniques, the support vector machine yielded the highest overall accuracy (98% for IOA, IOCG, skarn Cu-Fe, and porphyry Cu-Mo, and 97% for orogenic Au, VMS, SEDEX, and barren sedimentary pyrite) and thus is an appropriate technique in predicting pyrite types.

Statistical Analysis

10.2138/am-2023-8976

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Citations (4)

Ultramafic xenoliths from aillikites in the Tarim large igneous province: Implications for Alaskan-type affinity and role of subduction

Lithos (2020)

Linghan Liu Zhaochong Zhang Zhiguo Cheng M. Santosh Bingxiang Liu

Ultramafic rock

Amphibole

Xenolith

Petrogenesis

Mantle plume

Chromite

Large igneous province

Phlogopite

Fractional crystallization (geology)

10.1016/j.lithos.2020.105902

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The genetic link between iron oxide-apatite and iron skarn mineralization in the Beizhan deposit, Western Tianshan, NW China: Evidence from magnetite and gangue mineral geochemistry

Journal of Asian Earth Sciences (2022)

Hengxu Li Zhaochong Zhang Bingxiang Liu Yilun Jin M. Santosh

Ore genesis

Iron ore

Gangue

genetic model

Magmatic water

Felsic

10.1016/j.jseaes.2022.105460

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Citations (4)

Superimposed zinc and gold mineralization in the Dunde iron deposit, western Tianshan, NW China: Constraints from LA-ICP-MS fluid inclusion microanalysis

Ore Geology Reviews (2022)

Hengxu Li Zhaochong Zhang Bingxiang Liu Yilun Jin M. Santosh

The submarine volcanogenic iron oxide (SVIO) deposits are one of the most important sources of high-grade iron ores in China. Many large- and medium-scale SVIO deposits were recently discovered in the Awulale metallogenic belt (AMB) in Western Tianshan, NW China. The Dunde deposit in the AMB is a unique iron deposit containing large-scale Zn and medium-scale Au resources. The zinc mineralization in this deposit is generally located at the margin of iron orebodies constituting composite iron-zinc orebodies. To clarify the genetic relation between iron and Zn with Au mineralization, we conducted a detailed investigation of sphalerite-hosted fluid inclusions in the Dunde deposit. The primary fluid inclusions in sphalerite are liquid-rich, with homogenization temperature in the range of 189–219 ℃, and salinity of 19.68–22.38 wt% NaCl equiv. Single fluid inclusion LA-ICP-MS analysis suggests a magmatic source for the fluid as reflected by high Rb/Na (0.001–0.016), K/Na (0.18–0.83) and low Ca/K (1.03–9.38) ratios, which are distinctly different from those of basinal brines. The Cs/(K + Na + Ca)*10000 values (6.0–13.4) of sphalerite-hosted fluid inclusions are equivalent to those in porphyry-type Cu-Mo deposit (4.7–23.8), indicating that the ore-forming fluid originated from a highly evolved granitic magma, rather than intermediate-mafic magmatism related to iron mineralization. Thus, we propose that the zinc and gold mineralization in the Dunde deposit might be products of superimposed mineralization on iron orebodies during hydrothermal alteration related to granitic intrusions.

Ore genesis

10.1016/j.oregeorev.2022.104713

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Citations (5)

Machine Learning-Based Tectonic Discrimination Using Basalt Element Geochemical Data: Insights into the Carboniferous–Permian Tectonic Regime of Western Tianshan Orogen

Minerals (2025)

Hengxu Li Mengqi Gao Xiaohui Ji Zhaochong Zhang Zhiguo Cheng

Identifying the tectonic setting of rocks is essential for gaining insights into the geological contexts in which these rocks were formed, aiding in tectonic plate reconstruction and enhancing our comprehensive understanding of the Earth’s history. The application of machine learning algorithms helps identify complex patterns and relationships between big data that may be overlooked by binary or ternary tectonomagmatic discrimination diagrams based on basalt compositions. In this study, three machine learning algorithms, i.e., Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were employed to classify the basalts from seven diverse settings, including intraplate basalts, island arc basalts, ocean island basalts, mid-ocean ridge basalts, back-arc basin basalts, oceanic flood basalts, and continental flood basalts. Specifically, for altered and fresh basalt samples, we utilized 22 immobile elements and 35 major and trace elements, respectively, to construct discrimination models. The results indicate that XGBoost demonstrates the best performance in discriminating basalts into seven tectonic settings, achieving accuracies of 85% and 89% for the altered and fresh basalt samples, respectively. A key innovation of our newly developed tectonic discrimination model is the establishment of tailored models for altered and fresh basalts. Moreover, by omitting isotopic features during model construction, the new models offer broader applicability in predicting a wider range of basalt samples in practical scenarios. The classification models were applied to investigate the Carboniferous to Permian evolution in the Western Tianshan Orogen (WTO), revealing that the subduction of Tianshan Ocean ceased at the end of Carboniferous and the WTO evolved into a post-collisional orogenesis during the Permian.

10.3390/min15020122

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A machine learning-based approach using clinopyroxene data to improve accuracy and efficiency in predicting tectonic settings: Implications for Rodinia supercontinent breakup triggered by mantle plume events

American Mineralogist (2024)

Hengxu Li Zhaochong Zhang Ruixuan Zhang Marc Reichow Zhiguo Cheng

Basalts are ubiquitous mafic rocks found within diverse tectonic settings on Earth. Despite concerted efforts to distinguish tectonic settings through the chemical compositions of basalt, some features of these rocks related to tectonic processes can be obscured by weathering and erosion over geological time, making the discrimination results ambiguous. In this study, we utilized major and trace element data of clinopyroxene in basalts from five distinct tectonic settings: continental within-plate basalts (WPB), island arc basalts (IAB), ocean island basalts (OIB), oceanic floor basalts (OFB), and continental flood basalts (CFB). Employing three machine learning techniques—Multi-layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF)—we aim to discriminate tectonic settings and magma affinities. Sparse Multinomial Regression (SMR) approach is used to quantitatively discern geochemical signatures that are distinctive of each tectonic setting. The outcomes reveal the efficacy of SVM, which attains an accuracy of 92.1% (major element-based) and 95.2% (major and trace elements-based) for tectonic discrimination. Furthermore, SVM achieves an accuracy of 92.9% (major elements-based) and 95.7% (major and trace elements-based) for magma affinity discrimination. Our study shows that the integration of electron microprobe data from clinopyroxene with machine learning techniques provides an effective approach to distinguish various tectonic settings and magma affinities. The classifier models have been applied to investigate the Neoproterozoic geodynamics of the Jiangnan Orogen. The models show the two pulses of mantle plume events (∼830 Ma and ∼785 Ma) triggering break-up of the Rodinia supercontinent.

Rodinia

Supercontinent

Mantle plume

10.2138/am-2024-9535

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