Date of Award

5-17-2025

Document Type

Masters Project

Abstract

The declining rate of new metal discoveries, coupled with surging demand for critical minerals, underscores the need for improved geostatistical tools in early-stage exploration. Traditional fre- quentist methods like kriging are ill-suited for target generation due to their reliance on dense data, stationarity assumptions, and tendency to smooth anomalies. Meanwhile, exploration workflows remain largely qualitative, relying on geological intuition rather than probabilistic frameworks. A Bayesian spatial process convolution (SPC) model offers a promising alternative, leveraging hierarchical Bayesian formulations to model spatial processes with uncertainty quantification. This paper presents a Bayesian SPC model designed for geostatistical modeling, particularly in early-stage mineral exploration. The model is evaluated through simulations that mimic real-world geological complexity, including categorical boundaries and spatially coherent processes. The SPC model demonstrates its ability to recover smooth spatial structures and quantify uncertainty.

Handle

http://hdl.handle.net/11122/16323

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