A Nearest-Neighbour Gaussian Process Spatial Factor Model for Censored, Multi-Depth Geochemical Data. Issue 4 (19th May 2022)

• Record Type: Journal Article
• Authors: Davies, Tilman M.; Banerjee, Sudipto; Martin, Adam P.; Turnbull, Rose E.
• Is Part Of: Journal of the Royal Statistical Society. Volume 71:Issue 4(2022)
• Page Start: 1014
• Page End: 1043
• Publication Date: 2022-05-19
• Subjects: Bayesian model -- geochemistry -- geostatistics -- multivariate outcome -- spatial prediction; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12565
• Languages: English
• ISSNs: 0035-9254

Abstract

Abstract: We investigate the relationships between local environmental variables and the geochemical composition of the Earth in a region spanning over 26, 000 km 2 in the lower South Island of New Zealand. Part of the Southland–South Otago geochemical baseline survey—a pilot study pre-empting roll-out across the country—the data comprise the measurements of 59 chemical trace elements, each at two depth prescriptions, at several hundred spatial sites. We demonstrate construction of a hierarchical spatial factor model that captures inter-depth dependency; handles imputation of left-censored readings in a statistically principled manner; and exploits sparse approximations to Gaussian processes to deliver inference. The voluminous results provide a novel impression of the underlying processes and are presented graphically via simple web-based applications. These both confirm existing knowledge and provide a basis from which new research hypotheses in geochemistry might be formed.