Advancing censored geochemical Au prediction through Bayesian spatial models and Random Forest with fractal-based background separation
Hossein Mahdiyanfar
TL;DR
This paper introduces a new method combining Bayesian models and Random Forest to better predict gold concentrations in censored geochemical data.
Contribution
A novel multi-stage framework using Bayesian spatial modeling and fractal-based separation to handle censored geochemical data.
Findings
The RF–BGRF model outperforms traditional methods like LD-half and LD-rad2 in accuracy.
The framework preserves spatial variability and reduces detection-limit bias in low-range predictions.
Abstract
Censored geochemical data, particularly below detection limits, challenge mineral exploration by biasing anomaly delineation and spatial patterns. This study presents a multi-stage framework combining Bayesian Gaussian Random Field (BGRF) modeling with Random Forest (RF) learning, enhanced by fractal-based background separation, to accurately predict censored Au concentrations. 14 samples with gold concentrations below 5 ppb were hypothesized as censored data to enable a more accurate evaluation of the model’s performance based on their real Au concentrations. Unlike constant substitution methods, the framework preserves censored information and reconstructs spatial variability through probabilistic inference and nonlinear learning. The BGRF model incorporates spatial coordinates and Cu as the principal covariate to capture spatial autocorrelation and inter-element associations,…
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Taxonomy
TopicsGeochemistry and Geologic Mapping · Soil Geostatistics and Mapping · Mineral Processing and Grinding