TY - JOUR
T1 - 3-D geochemical interpolation guided by geophysical inversion models
AU - Horrocks, Tom
AU - Holden, Eun Jung
AU - Wedge, Daniel
AU - Wijns, Chris
PY - 2021/5
Y1 - 2021/5
N2 - 3-D geochemical subsurface models, as constructed by spatial interpolation of drill-core assays, are valuable assets across multiple stages of the mineral industry's workflow. However, the accuracy of such models is limited by the spatial sparsity of the underlying drill-core, which samples only a small fraction of the subsurface. This limitation can be alleviated by integrating collocated 3-D models into the interpolation process, such as the 3-D rock property models produced by modern geophysical inversion procedures, provided that they are sufficiently resolved and correlated with the interpolation target. While standard machine learning algorithms are capable of predicting the target property given these data, incorporating spatial autocorrelation and anisotropy in these models is often not possible. We propose a Gaussian process regression model for 3-D geochemical interpolation, where custom kernels are introduced to integrate collocated 3-D rock property models while addressing the trade-off between the spatial proximity of drill-cores and the similarities in their collocated rock properties, as well as the relative degree to which each supporting 3-D model contributes to interpolation. The proposed model was evaluated for 3-D modelling of Mg content in the Kevitsa Ni-Cu-PGE deposit based on drill-core analyses and four 3-D geophysical inversion models. Incorporating the inversion models improved the regression model's likelihood (relative to a purely spatial Gaussian process regression model) when evaluated at held-out test holes, but only for moderate spatial scales (100 m).
AB - 3-D geochemical subsurface models, as constructed by spatial interpolation of drill-core assays, are valuable assets across multiple stages of the mineral industry's workflow. However, the accuracy of such models is limited by the spatial sparsity of the underlying drill-core, which samples only a small fraction of the subsurface. This limitation can be alleviated by integrating collocated 3-D models into the interpolation process, such as the 3-D rock property models produced by modern geophysical inversion procedures, provided that they are sufficiently resolved and correlated with the interpolation target. While standard machine learning algorithms are capable of predicting the target property given these data, incorporating spatial autocorrelation and anisotropy in these models is often not possible. We propose a Gaussian process regression model for 3-D geochemical interpolation, where custom kernels are introduced to integrate collocated 3-D rock property models while addressing the trade-off between the spatial proximity of drill-cores and the similarities in their collocated rock properties, as well as the relative degree to which each supporting 3-D model contributes to interpolation. The proposed model was evaluated for 3-D modelling of Mg content in the Kevitsa Ni-Cu-PGE deposit based on drill-core analyses and four 3-D geophysical inversion models. Incorporating the inversion models improved the regression model's likelihood (relative to a purely spatial Gaussian process regression model) when evaluated at held-out test holes, but only for moderate spatial scales (100 m).
KW - Gaussian process regression
KW - Geophysical inversion
KW - Interpolation
KW - Kriging
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85099252996&partnerID=8YFLogxK
U2 - 10.1016/j.gsf.2020.09.018
DO - 10.1016/j.gsf.2020.09.018
M3 - Article
AN - SCOPUS:85099252996
SN - 1674-9871
VL - 12
JO - Geoscience Frontiers
JF - Geoscience Frontiers
IS - 3
M1 - 101089
ER -