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https://www.youtube.com/@valls_geoconsultant?sub_confirmation=1 For more videos about geology, geochemistry, AI, and much more, please visit and subscribe for free here: Golden droplets- https://shorturl.at/fetV1 Geovoices- https://tinyurl.com/m23pp4pb News about geology- https://tinyurl.com/3979urhy Geo News Radio: https://shorturl.at/MwdSK Timestamps 0:00 – Intro: From raw data to proven targets ​1:23 – Step 1: QA/QC and data reliability (duplicates, blanks, CRMs, lab checks) 3:47 – Step 2: Data prep, unit consistency, and handling BDL values 6:24 – Step 3: Molar conversions and element-by-element thinking 8:15 – Step 4: Normalization, Clark values, Fe–Mn effects, real vs false anomalies 10:05 – Step 5: Erosional level and vertical metal zoning around ore 11:41 – Step 6: Compositional Data Analysis and the closure problem 13:15 – Step 7: Multivariate statistics (PCA, HCA) to define vectors to ore 14:39 – Step 8: Quantic maps, overlapping anomalies, and final drill targets Learn how to transform raw laboratory data into proven mineral exploration targets by following a structured geochemical data analysis workflow that ensures scientific rigor and defensible geological interpretations. This tutorial covers essential modern techniques, including quality assurance and quality control (QA/QC), molar conversion for mineral stoichiometry, and compositional data analysis (CoDA) to solve the closure problem. The process begins with building trust in your data through the application of proactive QA systems and reactive QC checks, utilizing field duplicates, blanks, and certified reference materials to monitor for contamination, bias, and imprecision (Geboy & Engle, 2011). Because geological processes like hydrothermal alteration and mineral formation are inherently atomic and stoichiometric, this guide explains why you must convert mass-based units like ppm into molar concentrations to link data trends directly to balanced chemical reactions (Stanley, 2006). Furthermore, as geochemical data are constrained by a constant sum, you will learn to apply log-ratio transformations—such as centered (CLR) or isometric (ILR) log-ratios—to map compositional data into real Euclidean space, thereby avoiding the spurious correlations that often plague raw datasets (Aitchison, 1986; Pawlowsky-Glahn & Egozcue, 2006). Complexity in multi-element surveys is reduced using multivariate statistical methods like Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), which organize high-dimensional variance into interpretable geochemical associations and sample groupings (Jolliffe & Cadima, 2016; Everitt et al., 2011). The workflow culminates in the creation of robust geochemical maps, often utilizing the non-parametric quartile method to define "quantic" anomalies that remain stable in the presence of outliers or "hurricane values" (Reimann et al., 2008). By integrating these advanced techniques, exploration geologists can define defensible drill targets that are validated by mineralogical knowledge and structural context. 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 If you like this content, please "buy me a coffee" https://www.buymeacoffee.com/goldendroplets #vallsgeoconsultant #GeochemicalDataAnalysis #MineralExploration #CompositionalDataAnalysis Some references Aitchison, J. (1986). The statistical analysis of compositional data. Chapman & Hall. Baccarelli, A., Pfeiffer, R., Consonni, D., Pesatori, A. C., Bonzini, M., Patterson Jr, D. G., ... & Landi, M. T. (2005). Handling of dioxin measurement data in the presence of non-detectable values: Overview of available methods and their application in the Seveso chloracne study. Chemosphere, 60(7), 898-906. Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barcelo-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300. Grunsky, E. C., & de Caritat, P. (2019). State-of-the-art analysis of geochemical data for mineral exploration. Geochemistry: Exploration, Environment, Analysis, 19(2), 1–16. Grunsky, E. C., Greenacre, M., & Kjarsgaard, B. A. (2023). GeoCoDA: Recognizing and validating structural processes in geochemical data—A workflow on compositional data analysis in lithogeochemistry. Applied Computing and Geosciences, 22, 100149. Pawlowsky-Glahn, V., & Egozcue, J. J. (2006). Compositional data and their analysis: An introduction. Geological Society, London, Special Publications, 264(1), 1–10.