A Bayesian method for estimating uncertainty in excavated material. Issue 2 (7th February 2022)

• Record Type: Journal Article
• Authors: Balamurali, Mehala
• Is Part Of: International journal of mining, reclamation and environment. Volume 36:Issue 2(2022)
• Page Start: 125
• Page End: 141
• Publication Date: 2022-02-07
• Subjects: Gaussian mixture model -- moment matching -- uncertainty tracking -- iron ore mining -- reclamation -- scooped material; Mining engineering -- Periodicals; Mineral industries -- Environmental aspects -- Periodicals; Abandoned mined lands reclamation -- Periodicals; 622.292
• DOI: 10.1080/17480930.2021.1992103
• Languages: English
• ISSNs: 1748-0930

Abstract

ABSTRACT: This paper proposes a method to probabilistically quantify the moments (mean and variance) of excavated material during excavation by aggregating the prior moments of the grade blocks around the given bucket dig location. By modelling the moments as random probability density functions (pdf) at sampled locations, a formulation of the sums of Gaussian-based uncertainty estimation is presented that jointly estimates the location pdfs, as well as the prior values for uncertainty coming from ore body knowledge (obk) sub-block models. The method was tested in a region situated in the Brockman Iron Formation of the Hamersley Province, Western Australia.