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Compositional Balance Analysis: An Elegant Method of Geochemical Pattern Recognition and Anomaly Mapping for Mineral Exploration

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Abstract

Geochemical pattern recognition and anomaly mapping are always involved in the fields of environmental and exploration geochemistry. Principal component analysis (PCA) and factor analysis (FA) are most commonly used to reveal underlying geochemical associations for the purpose of spatial distribution pattern analysis. However, the methods of PCA and FA cannot eliminate correlations between different principal components/factors, meaning that geochemical associations revealed by PCA or FA could be simultaneously influenced by two or more principal components/factors, as can be observed from biplot analysis. Such problem provides a challenge for interpretation of geochemical/geological processes. In the present study, we demonstrated a simple method, termed compositional balance analysis (CoBA), to interpret critical geochemical/geological processes. Comparative studies between CoBA and compositional factor analysis, as well as data- and knowledge-driven CoBA, were considered to discuss the advantage and practicability of the CoBA in geochemical pattern recognition and anomaly mapping based on a case study in the Nanling belt, South China. The results indicate that the CoBA has greater efficiency in enhancing weak or concealed geochemical anomalies and suppressing spurious geochemical anomalies relative to multivariate dimensionality reduction analysis; especially, knowledge-driven CoBA provides more robust interpretation of geochemical/geological processes relative to data-driven CoBA.

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References

  • Aitchison, J. (1986). The statistical analysis of compositional data. London: Chapman and Hall.

    Book  Google Scholar 

  • Bao, Z., & Zhao, Z. (2008). Geochemistry of mineralization with exchangeable REY in the weathering crusts of granitic rocks in South China. Ore Geology Reviews, 33(3–4), 519–535.

    Article  Google Scholar 

  • Boente, C., Albuquerque, M. T. D., Fernández-Braña, A., Gerassis, S., Sierra, C., & Gallego, J. R. (2018). Combining raw and compositional data to determine the spatial patterns of Potentially Toxic Elements in soils. Science of the Total Environment, 631, 1117–1126.

    Article  Google Scholar 

  • Buccianti, A., Egozcue, J. J., & Pawlowsky-Glahn, V. (2014). Variation diagrams to statistically model the behavior of geochemical variables: Theory and applications. Journal of Hydrology, 519, 988–998.

    Article  Google Scholar 

  • Buccianti, A., & Grunsky, E. (2014). Compositional data analysis in geochemistry: Are we sure to see what really occurs during natural processes? Journal of Geochemical Exploration, 141, 1–5.

    Article  Google Scholar 

  • Buccianti, A., Lima, A., Albanese, S., & De Vivo, B. (2018). Measuring the change under compositional data analysis (CoDA): Insight on the dynamics of geochemical systems. Journal of Geochemical Exploration, 189, 100–108.

    Article  Google Scholar 

  • Buccianti, A., & Zuo, R. (2016). Weathering reactions and isometric log-ratio coordinates: Do they speak to each other? Applied Geochemistry, 75, 189–199.

    Article  Google Scholar 

  • Carranza, E. J. M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110(2), 167–185.

    Article  Google Scholar 

  • Carranza, E. J. M. (2017). Geochemical mineral exploration: Should we use enrichment factors or log-ratios? Natural Resources Research, 26, 411–428.

    Article  Google Scholar 

  • Egozcue, J. J., Lovell, D., & Pawlowsky-Glahn, V. (2013). Testing compositional association. In: K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th international workshop on compositional data analysis (pp. 28–36). Vorau, Austria.

  • Egozcue, J. J., & Pawlowsky-Glahn, V. (2005). Groups of parts and their balances in compositional data analysis. Mathematical Geology, 37, 795–828.

    Article  Google Scholar 

  • Egozcue, J. J., & Pawlowsky-Glahn, V. (2018). Modelling compositional data. The sample space approach. In: Handbook of mathematical geosciences (pp. 81–103). Cham: Springer.

  • Egozcue, J. J., Pawlowsky-Glahn, V., & Gloor, G. B. (2018). Linear association in compositional data analysis. Austrian Journal of Statistics, 47, 3–31.

    Article  Google Scholar 

  • Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barcelo-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35, 279–300.

    Article  Google Scholar 

  • Filzmoser, P., Hron, K., & Reimann, C. (2009a). Principal component analysis for compositional data with outliers. Environmetrics: The Official Journal of the International Environmetrics Society, 20(6), 621–632.

    Article  Google Scholar 

  • Filzmoser, P., Hron, K., Reimann, C., & Garrett, R. (2009b). Robust factor analysis for compositional data. Computers & Geosciences, 35(9), 1854–1861.

    Article  Google Scholar 

  • Filzmoser, P., Hron, K., & Tolosana-Delgado, R. (2016). Statistical analysis of geochemical compositions: Problems, perspectives and solutions. Applied Geochemistry, 75, 169–170.

    Article  Google Scholar 

  • Fišerová, E., & Hron, K. (2011). On the interpretation of orthonormal coordinates for compositional data. Mathematical Geosciences, 43(4), 455–468.

    Article  Google Scholar 

  • Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.

    Article  Google Scholar 

  • Graffelman, J., Pawlowsky-Glahn, V., Egozcue, J. J., & Buccianti, A. (2018). Exploration of geochemical data with compositional canonical biplots. Journal of Geochemical Exploration, 194, 120–133.

    Article  Google Scholar 

  • Grunsky, E. C., De Caritat, P., & Mueller, U. A. (2017). Using surface regolith geochemistry to map the major crustal blocks of the Australian continent. Gondwana Research, 46, 227–239.

    Article  Google Scholar 

  • Grunsky, E. C., Mueller, U. A., & Corrigan, D. (2014). A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: Applications for predictive geological mapping. Journal of Geochemical Exploration, 141, 15–41.

    Article  Google Scholar 

  • Hron, K., Filzmoser, P., de Caritat, P., Fišerová, E., & Gardlo, A. (2017). Weighted pivot coordinates for compositional data and their application to geochemical mapping. Mathematical Geosciences, 49(6), 797–814.

    Article  Google Scholar 

  • Karacan, C. Ö., & Olea, R. A. (2018). Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation: A comparative study for spatial ultimate analyses data. Journal of Geochemical Exploration, 186, 36–49.

    Article  Google Scholar 

  • Liu, Y., Cheng, Q., Xia, Q., & Wang, X. (2014a). Mineral potential mapping for tungsten polymetallic deposits in the Nanling metallogenic belt, South China. Journal of Earth Science, 25(4), 689–700.

    Article  Google Scholar 

  • Liu, Y., Cheng, Q., Xia, Q., & Wang, X. (2014b). Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China. Environmental Earth Sciences, 72(12), 5159–5169.

    Article  Google Scholar 

  • Liu, Y., Cheng, Q., Zhou, K., Xia, Q., & Wang, X. (2016). Multivariate analysis for geochemical process identification using stream sediment geochemical data: A perspective from compositional data. Geochemical Journal, 50(4), 293–314.

    Article  Google Scholar 

  • Liu, Y., Xia, Q., & Carranza, E. J. M. (2019). Integrating sequential indicator simulation and singularity analysis to analyze uncertainty of geochemical anomaly for exploration targeting of tungsten polymetallic mineralization, Nanling belt, South China. Journal of Geochemical Exploration, 197, 143–158.

    Article  Google Scholar 

  • Liu, Y., Zhou, K., & Carranza, E. J. M. (2018a). Compositional balance analysis for geochemical pattern recognition and anomaly mapping in the western Junggar region, China. Geochemistry: Exploration, Environment, Analysis, 18, 263–276.

    Google Scholar 

  • Liu, Y., Zhou, K., & Xia, Q. (2018b). A MaxEnt model for mineral prospectivity mapping. Natural Resources Research, 27(3), 299–313.

    Article  Google Scholar 

  • Mao, J., Xie, G., Guo, C., & Chen, Y. (2007). Large-scale tungsten-tin mineralization in the Nanling region South China: Metallogenic ages and corresponding geodynamic processes. Acta Petrologica Sinica, 23(10), 2329–2338. (in Chinese with English Abstract).

    Google Scholar 

  • Martín-Fernández, J. A., Olea, R. A., & Ruppert, L. F. (2018a). Compositional data analysis of coal combustion products with an application to a Wyoming power plant. Mathematical Geosciences, 50(6), 639–657.

    Article  Google Scholar 

  • Martín-Fernández, J. A., Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosona-Delgado, R. (2018b). Advances in principal balances for compositional data. Mathematical Geosciences, 50(3), 273–298.

    Article  Google Scholar 

  • McKinley, J. M., Grunsky, E., & Mueller, U. (2018). Environmental monitoring and peat assessment using multivariate analysis of regional-scale geochemical data. Mathematical Geosciences, 50(2), 235–246.

    Article  Google Scholar 

  • McKinley, J. M., Hron, K., Grunsky, E. C., Reimann, C., de Caritat, P., Filzmoser, P., et al. (2016). The single component geochemical map: Fact or fiction? Journal of Geochemical Exploration, 162, 16–28.

    Article  Google Scholar 

  • Otero, N., Tolosana-Delgado, R., Soler, A., Pawlowsky-Glahn, V., & Canals, A. (2005). Relative vs. absolute statistical analysis of compositions: a comparative study of surface waters of a Mediterranean river. Water Research, 39(7), 1404–1414.

    Article  Google Scholar 

  • Parent, L. E., Parent, S. É., & Ziadi, N. (2014). Biogeochemistry of soil inorganic and organic phosphorus: A compositional analysis with balances. Journal of Geochemical Exploration, 141, 52–60.

    Article  Google Scholar 

  • Pawlowsky-Glahn, V., & Buccianti, A. (2011). Compositional data analysis, theory and applications. Chichester: Wiley.

    Book  Google Scholar 

  • Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana Delgado, R. (2007). Lecture notes on compositional data analysis (pp. 1–87). Spain: Universitat De Girona. Available at https://dugi-doc.udg.edu/bitstream/handle/10256/297/?sequence=1.

  • Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2011). Principal balances. In: J. J. Egozcue, R. Tolosana-Delgado, & M. Ortego (Eds.) Proceedings of the 4th international workshop on compositional data analysis (pp. 1–10). Girona, Spain.

  • Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2015). Modeling and analysis of compositional data. Chichester: Wiley.

    Google Scholar 

  • Peng, J., Zhou, M., Hu, R., Shen, N., Yuan, S., Bi, X., et al. (2006). Precise molybdenite Re–Os and mica Ar–Ar dating of the Mesozoic Yaogangxian tungsten deposit, central Nanling district, South China. Mineralium Deposita, 41, 661–669.

    Article  Google Scholar 

  • Petrik, A., Thiombane, M., Lima, A., Albanese, S., Buscher, J. T., & De Vivo, B. (2018). Soil contamination compositional index: A new approach to quantify contamination demonstrated by assessing compositional source patterns of potentially toxic elements in the Campania Region (Italy). Applied Geochemistry, 96, 264–276.

    Article  Google Scholar 

  • Reimann, C., Filzmoser, P., Garrett, R., & Dutter, R. (2008). Statistical data analysis explained: Applied environmental statistics with R. Chichester: Wiley.

    Book  Google Scholar 

  • Reimann, C., Filzmoser, P., Hron, K., Kynčlová, P., & Garrett, R. G. (2017). A new method for correlation analysis of compositional (environmental) data–a worked example. Science of the Total Environment, 607, 965–971.

    Article  Google Scholar 

  • Rivera-Pinto, J., Egozcue, J. J., Pawlowsky-Glahn, V., Paredes, R., Noguera-Julian, M., & Calle, M. L. (2018). Balances: A new perspective for microbiome analysis. mSystems, 3(4), e00053-18.

    Article  Google Scholar 

  • Shu, L., Zhou, X., Deng, P., & Yu, X. (2006). Principal geological features of Nanling Tectonic Belt, South China. Geological Review, 52, 251–265. (in Chinese with English abstract).

    Google Scholar 

  • Talebi, H., Mueller, U., Tolosana-Delgado, R., Grunsky, E. C., McKinley, J. M., & de Caritat, P. (2018). Surficial and deep earth material prediction from geochemical compositions. Natural Resources Research. https://doi.org/10.1007/s11053-018-9423-2.

    Google Scholar 

  • Thiombane, M., Di Bonito, M., Albanese, S., Zuzolo, D., Lima, A., & De Vivo, B. (2019). Geogenic versus anthropogenic behaviour and geochemical footprint of Al, Na, K and P in the Campania region (Southern Italy) soils through compositional data analysis and enrichment factor. Geoderma, 335, 12–26.

    Article  Google Scholar 

  • Van den Boogaart, K. G., & Tolosana-Delgado, R. (2013). Analyzing compositional data with R. Use R!. Berlin: Springer.

    Book  Google Scholar 

  • Wang, X., Ni, P., Yuan, S., & Wu, S. (2012). Fluid inclusion studies of the Huangsha quartz-vein type tungsten deposit, Jiangxi province. Acta Petrologica Sinica, 28, 122–132. (in Chinese with English abstract).

    Google Scholar 

  • Wang, X., Zhang, Q., & Zhou, G. (2007). National-scale geochemical mapping projects in China. Geostandards and Geoanalytical Research, 31(4), 311–320.

    Article  Google Scholar 

  • Wu, D., Lu, H., Xu, L., & Hou, L. (1993). A preliminary study on modes of occurrence of rare earth elements in the tropical-subtropical weathering crust of Nanling region. Mineral Deposits, 12, 297–305. (in Chinese with English abstract).

    Google Scholar 

  • Xie, X., Mu, X., & Ren, T. (1997). Geological mapping in China. Journal of Geochemical Exploration, 60, 99–113.

    Article  Google Scholar 

  • Xu, X., O’Reilly, S. Y., Griffin, W. L., Deng, P., & Pearson, N. J. (2005). Relict Proterozoic basement in the Nanling Mountains (SE China) and its tectonothermal overprinting. Tectonics, 24, TC2003.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69–81.

    Article  Google Scholar 

  • Zhao, W., Zhou, M., Li, Y., Zhao, Z., & Gao, J. (2017). Genetic types, mineralization styles, and geodynamic settings of Mesozoic tungsten deposits in South China. Journal of Asian Earth Sciences, 137, 109–140.

    Article  Google Scholar 

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Acknowledgment

This work was funded jointly by the National Natural Science Foundation of China (Nos. 41672328, 41702356). The authors are grateful to Dr. Antonella Buccianti and an anonymous reviewer for their constructive comments, which greatly helped us improve the presentation of material in this paper.

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Authors and Affiliations

  1. Xinjiang Research Centre for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China

    Yue Liu & Kefa Zhou

  2. Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi, 830011, Xinjiang, China

    Yue Liu & Kefa Zhou

  3. Geological Sciences, School of Agriculture, Engineering and Sciences, University of KwaZulu-Natal, Westville, South Africa

    Emmanuel John M. Carranza

  4. Cooperative Innovation Center for Scarce Mineral Resources Exploration, China University of Geosciences (Wuhan), Wuhan, 430074, Hubei, China

    Qinglin Xia

  5. Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China

    Qinglin Xia

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  1. Yue Liu
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  2. Emmanuel John M. Carranza
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  3. Kefa Zhou
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  4. Qinglin Xia
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Correspondence to Yue Liu.

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Liu, Y., Carranza, E.J.M., Zhou, K. et al. Compositional Balance Analysis: An Elegant Method of Geochemical Pattern Recognition and Anomaly Mapping for Mineral Exploration. Nat Resour Res 28, 1269–1283 (2019). https://doi.org/10.1007/s11053-019-09467-8

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