VisualEasier - An enhanced visualization tool for mineral chemistry data analysis

Main Article Content

Lucas Abud de Mesquita
Guilherme Ferreira da Silva
https://orcid.org/0000-0002-3675-7289
Joseneusa Brilhante Rodrigues

Abstract

VisualEasier is a user-friendly application designed to improve the visualization and interpretation of spatially resolved geochemical data. Developed in  R environment  with the Shiny package, it provides a dynamic interface that allows users, regardless of programming experience, to generate high-resolution elemental maps, apply image filters, and perform clustering analysis. The application supports datasets from  varied analytical techniques, such as SEM-EDS, Electron Microprobe, Micro-XRF, and LA-ICP-MS, as long as the input is as comma-separated values with pixel-level positional reference. Users can produce  elemental maps with pixel-by-pixel normalization, generate ternary RGB compositions to explore elemental associations, and apply median or gradient filters to enhance visual features such as grain boundaries or reduce noise. The clustering module  uses the k-means algorithm  to organize the sample into user-defined compositional groups, returning a set of outputs that includes spatial maps, statistical tables, and boxplots for each cluster. All graphical findings can be  imported as high-quality vectorized PDF files. The system’s flexibility allows users to input normalization references,  color palettes, and  visualization scales. Application on real samples demonstrated the effectiveness of the application in  uncovering compositional patterns that are not easily identifiable using conventional static plots. The clustering results provided insights into mineralogical domains and potential zoning within the sample, while the filter tools improved the clarity of textural and chemical boundaries. The combination of interactive features, compatibility with multiple data sources, and customizable outputs makes VisualEasier a versatile tool for geologists, mineralogists, and materials scientists looking to  improve data interpretation. The application is particularly valuable in exploratory and research environments where rapid feedback and visual diagnostics play a critical role in decision-making and hypothesis generation.

Article Details

How to Cite
Abud, Lucas, Guilherme Ferreira da Silva, and Joseneusa Brilhante Rodrigues. 2024. “VisualEasier - An Enhanced Visualization Tool for Mineral Chemistry Data Analysis”. Journal of the Geological Survey of Brazil 8 (3). https://doi.org/10.29396/jgsb.2025.v8.n3.2.
Section
Accepted Manuscripts

References

Barret, T., Dowle, M., Srinivasan, A., Gorecki, J., Chirico, M., Hocking, T., Scwendinger, B., Krylov, I., 2025. data.table: Extension of “data.frame”, Computer Program, Version 1.17.99. Available online at: https://r-datatable.com / (accessed on 14 July 2025). DOI 10.32614/CRAN.package.data.table

Bérubé, C.L., Olivo, G.R., Chouteau, M., Perrouty, S., Shamsipour, P., Enkin, R.J., Morris, W.A., Feltrin, L., Thiémonge, R., 2018. Predicting rock type and detecting hydrothermal alteration using machine learning and petrophysical properties of the Canadian Malartic ore and host rocks, Pontiac Subprovince, Québec, Canada. Ore Geol. Rev. 96, 130–145. https://doi.org/10.1016/j.oregeorev.2018.04.011

Caté, A., Perozzi, L., Gloaguen, E., Blouin, M., 2017. Machine learning as a tool for geologists. Lead. Edge 36, 215–219. https://doi.org/10.1190/tle36030215.1

Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., Borges, B., 2025. shiny: Web Application Framework for R, Computer Program, Version 1.11.1; Available at: https://shiny.posit.co/ / (accessed on 14 July 2025). DOI 10.32614/CRAN.package.shiny

Ferreira da Silva, G., Ferreira, M.V., Costa, I.S.L., Bernardes, R.B., Mota, C.E.M., Cuadros Jiménez, F.A., 2021. Qmin – A machine learning-based application for processing and analysis of mineral chemistry data. Comput. Geosci. 157, 104949. https://doi.org/10.1016/j.cageo.2021.104949

Ferreira da Silva, G., Larizzatti, J.H., da Silva, A.D.R., Lopes, C.G., Klein, E.L., Uchigasaki, K., 2022a. Unsupervised drill core pseudo-log generation in raw and filtered data, a case study in the Rio Salitre greenstone belt, São Francisco Craton, Brazil. J. Geochemical Explor. 232. https://doi.org/10.1016/j.gexplo.2021.106885

Ferreira da Silva, G., Silva, A.M., Toledo, C.L.B., Chemale Junior, F., Klein, E.L., 2022b. Predicting mineralization and targeting exploration criteria based on machine-learning in the Serra de Jacobina quartz-pebble-metaconglomerate Au-(U) deposits, São Francisco Craton, Brazil. J. South Am. Earth Sci. 116. https://doi.org/10.1016/j.jsames.2022.103815

Figueira, M., Conesa, D., López-Quílez, A., 2024. A shiny R app for spatial analysis of species distribution models. Ecol. Inform. https://doi.org/10.1016/j.ecoinf.2024.102542

Gazley, M.F., Duclaux, G., Fisher, L.A., Tutt, C.M., Latham, A.R., Hough, R.M., De Beer, S.J., Taylor, M.D., 2015. A comprehensive approach to understanding ore deposits using portable x-ray fluorescence (Pxrf) data at the plutonic gold mine, western australia. Geochemistry Explor. Environ. Anal. 15, 113–124. https://doi.org/10.1144/geochem2014-280

Hartigan, J.A., Wong, M.A., 1979. A K-Means Clustering Algorithm. J. R. Stat. Soc. Ser. C (Applied Stat. 28, 100–108. https://doi.org/10.9756/bijdm.1106

Hijmans, R., 2025. raster: Geographic Data Analysis and Modeling. Computer Program, Version 3.6-32, Available online at: https://rspatial.org/raster/ / (accessed on 14 July 2025). DOI 10.32614/CRAN.package.raster.

Konrath, A.C., Silva, S.A. da, Henning, E., Santos, L.M. dos, Miranda, R.G. de, Samohyl, R.W., 2018. Desenvolvimento de Aplicativos Web Com R e Shiny: inovações no ensino de Estatística. Abakós 6, 55–71. https://doi.org/10.5752/p.2316-9451.2018v6n2p55-71

Möller, M., Boutarfa, L., Strassemeyer, J., 2020. PhenoWin – An R Shiny application for visualization and extraction of phenological windows in Germany. Comput. Electron. Agric. 175, 105534. https://doi.org/10.1016/j.compag.2020.105534

Wickham, H., 2016. ggplot2, Use R! Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-24277-4

Wickham, H., 2014. Tidy Data. J. Stat. Softw. 59. https://doi.org/10.18637/jss.v059.i10