Don't know where your data is from? Bayesian modeling for unknown coordinates
The article discusses the application of Bayesian modeling in the mining industry, particularly for predicting mineral concentrations at unknown coordinates. It highlights the challenges posed by spatial correlation and measurement noise in geophysical modeling. The use of Gaussian process models is explored, demonstrating how Bayesian methods can adapt to these uncertainties.
- ▪Bayesian modeling is used to address challenges in predicting mineral concentrations during exploration.
- ▪Spatial correlation and measurement noise complicate the construction of geophysical models.
- ▪Gaussian process models can be modified to accommodate unknown coordinates and measurement errors.
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An especially strong motivating case for the usage of spatial probability models comes from the mining industry. During exploration for mineral resources, prospectors will take geologic samples by drilling holes and examining the resulting material for presence or concentration of valuable ores. These data typically show strong spatial correlation, but constructing a fully-detailed geophysical model is at times infeasible as we are able to observe very little of the underground conditions, though the advent of remote sensing techniques like ground-penetrating radar and gravimetry has dramatically improved our ability to characterize Earth’s subsurface.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Christopherkrapu.
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