Monday, January 5, 2026
https://youtu.be/tXa5RNqoL-s
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Timestamps
00:00 – Intro: why correlation misleads in geochemistry
00:36 – Closure problem and spurious correlations
02:36 – Why standard stats fail on compositional data
03:21 – CoDA and log‑ratio transformations (ALR, CLR, ILR)
06:38 – “High correlation” vs real geological meaning
07:13 – Case studies: soils, Shadan district, Lisheen mine
09:01 – Zeros and below detection limits
10:02 – Tools and machine learning gains
11:36 – Compositional mindset and closing remarks
Stop using traditional correlation analysis in your geochemical exploration and mineral prospectivity mapping because the closure problem in compositional data analysis (CoDA) often generates misleading spurious correlations (Aitchison, 1982; Pacifico et al., 2025). To accurately identify ore bodies and mineral deposits using machine learning and artificial intelligence, geologists must transition from absolute concentrations to log-ratio transformations (Egozcue et al., 2003; Valls, 2024). Standard statistical methods like Pearson’s correlation coefficient assume variables are independent, but geochemical measurements are reported as proportions that sum to a constant total, meaning they are part of a closed system where variables are intrinsically dependent (Filzmoser et al., 2018; Greenacre, 2021).
The bridge between Academy and Industry!
P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly
Valls Geoconsultant
ORCID ID- https://orcid.org/0000-0002-5421-0914
Scopus Author ID: 7003369619/35335510700
ResearcherID: S-6604-2018
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#valls_geoconsultant #Geochemistry #Exploration #CompositionalDataAnalysis
Some references
• Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological), 44(2), 139–160.
• Bacon-Shone, J. (2008). Book review: A. Buccianti, G. Mateu-Figueras and V. Pawlowsky-Glahn (eds): Compositional data analysis in the geosciences: from theory to practice. Stochastic Environmental Research and Risk Assessment, 22(1), 139–141.
• Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barceló-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300.
• Tolosana-Delgado, R., Mueller, U., van den Boogaart, K. G., Pawlowsky-Glahn, V., & Egozcue, J. J. (2019). Geostatistics with compositional data, an overview. Mathematical Geosciences, 51, 485–526.
• Valls, R. (n.d.). Compositional Data Analysis & Outliers: A New Way to Find Your Next Deposit [Video]. YouTube.
• Valls, R. (n.d.). Compositional data analysis for geochemists... and doctors? [Video]. YouTube.
• Valls, R. (n.d.). Compositional Data Analysis: Why Your Standard Statistics Are Wrong! [Video]. YouTube.
• Valls, R. (2024, August 8). Compositional Data Analysis: A Comprehensive Guide to Using CoDaPack in Geochemistry. OSF Preprint. DOI: 10.17605/OSF.IO/NYVCJ.
• Valls, R. (n.d.). Exploring geological data with WEKA, CodaPack, and iNZight [Video]. YouTube.
• Valls, R. (2021, March). Golden Droplets Episode 23: Using compositional data analysis [Video]. YouTube. https://youtu.be/k6Qcti9BHfE
• Valls, R. (2020, January). Golden Droplets Episode 10: Using CoDaPack part Two [Video]. YouTube.
• Valls, R. (n.d.). Golden Droplets Episode 8: Compositional Data Analysis [Video]. YouTube.
• Valls, R. (n.d.). Golden Droplets Episode 9: Using CoDaPack [Video]. YouTube.
• Valls, R. (2024, August). Say Goodbye to Spurious Correlations: Mastering Compositional Data Analysis for Geologists [Video]. YouTube. https://youtu.be/BN14rau2_xc
• Valls, R. (n.d.). The Data Challenge: Why Traditional Geochemistry Fails & How CoDA Steps Up [Video]. YouTube.
• Valls, R. (2018, July). Why and How Should Geologists Use Compositional Data Analysis [Preprint excerpts].
• Valls, R. (n.d.). Why, and How, Should Geologists Use CoDa v2 shorter with video [Video]. YouTube.
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