Variability characterization is a key component in the reliability assessment of geotechnical systems, particularly in scenarios involving tailings deposits, where spatial heterogeneity can critically impact design safety. Despite advances in modeling spatial randomness, many reported cases still rely on theoretical assumptions to define appropriate statistical characterizations—such as theoretical probability density functions (PDFs) and correlation structures—which may misrepresent site-specific conditions. To bridge this gap, piezocone penetration tests (CPTu) stand out as promising tools for providing continuous measurements along a vertical profile that can be used to define statistical behavior and avoid bias. This paper presents a spatial variability characterization of a bauxite tailings deposit based on mechanical parameters derived from CPTu data. The study includes basic statistical analysis—mean (μ), standard deviation (s), coefficient of variation (CV)—alongside a comparison with theoretical PDFs. Subsequently, spatial correlation is evaluated through covariance analysis and estimation of the vertical scale of fluctuation (δ), using a dedicated subroutine that fits theoretical autocorrelation models (TAMs). The deposit is classified as highly variable according to the I_cRW index. The normal and Weibull PDFs best represent the data distributions. The vertical scales of fluctuation vary significantly: 0.01 m to 4.43 m for cone resistance (q_c), 0.01 m to 4.36 m for sleeve friction (f_s), and 0.01 m to 5.00 m for pore water pressure (u₂). These findings offer valuable input for probabilistic stability and serviceability analyses, contributing to safer and more informed geotechnical designs involving mine tailings.

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