Fig 1.
Theoretical origin of the bauxite deposits is associated with ash deposition from ancient volcanic activity in Central America.
The yellow polygon demarcates the proposed zone of ash deposition, where the bauxite deposits exhibit geochemical similarities (adapted from Goldich, 1947).
Fig 2.
(A) Map showing the distribution of sampling sites; (B) Image showing natural horizonation of the bauxite deposits from a soil profile cutout created by the landowner. Samples were collected at the surface and the subsurface, with the strong color change, indicating the natural boundary between the typical clay loam A horizon and the argillic (clay) subsurface horizons (Bt1 and Bt2).
Table 1.
Samples collected by bauxite deposit group with their corresponding geospatial coordinates and elevation information.
Table 2.
List of candidate models and hyperparameters used for preliminary model selection.
A range of values was assigned for each hyperparameter. The Scikit-Learn ‘GridSearch_CV’ function, was used to create a grid of all possible combinations of these hyperparameters. Cross-validation was then applied to evaluate model performance for each combination.
Fig 3.
Different visualizations of the geochemical data.
Ternary plots showing the relationship among Si, Al, and Fe concentrations (as determined by pXRF analysis of 10 different spots on powderized samples) with respect to (A) Deposit group and (B) Order in the NRCS soil morphological classification system. (C) UMAP decomposition of geochemical data determined by acid-digested, ICP-OES-determined REE showing the latent structural relationships among the different bauxite deposits.
Fig 4.
Boxplots showing the statistical distributions of the REE data.
(A) the measured log10 REE concentration and (B) LREE/HREE ratios in the soils with respect to each particular deposit group for both the surface and subsurface depths.
Fig 5.
Correlation plot showing the relationships between soil REE concentrations, LREE/HREE ratios, and the geographical features of sampled bauxite deposits.
Median soil REE concentrations are shown for both the surface and subsurface depths.
Fig 6.
Plot showing the relationship between soil pH and soil redox potential for the different bauxite deposits.
Fig 7.
Prediction plots from the ExtraTrees regression model predicting soil REE based on (A) bulk composition, (B) pXRF analysis, and (C) NixPro2 color sensor optimized validation data. The red dotted line represents the model’s function, with shading indicating the standard error. The black line represents the true diagonal of observed and predicted values.
Table 3.
Generalization estimates for the soil characterization data (including REE) based on the the three sets of data (bulk composition, pXRF, and color sensor).
Root mean squared errors (RMSE) and the coefficient of determination (R2) are provided for both the average and optimized test-train splits, calculating using a binned stratification resampling procedure to reduce potential bias from the train-test split in the Extremely Randomized Trees (ET) model. The values represent the results after 500 sampling iterations, illustrating both the expected (average RMSE) and best-case (optimized RMSE) model performance.
Fig 8.
Preliminary vis-NIR spectra for the Aceitillar2 sample.
The first derivative transformation of the spectra indicates the chemical domains contained within the sample based on the wavelengths of its “peaks” and “valleys”.