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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen Yu Xianchuan Yu Jingru Hou |
| Copyright Year | 2004 |
| Description | Author affiliation: Coll. of Inf. Sci., Beijing Normal Univ. (Chen Yu; Xianchuan Yu) |
| Abstract | Linear estimation methods such as ordinary and simple kriging commonly fail to provide unbiased estimates of recovered ore tonnage and metal content which means that a mining project can be exposed to undue risk. Nonlinear estimation, such as the Gaussian disjunctive kriging (DK) technique provide a mean of calculating unbiased estimates of ore and metal content over any cut-off range and mining unit size combination. The disadvantage of this method is the requirement of an assumption of strict stationarity. It supposes that we have known all bivariate distributions of regional variables $(Z_{alpha},$ Z $_{beta})$ and $(Z_{0},$ $Z_{beta})$ in which the values of $Z_{alpha},$ $Z_{beta}$ is known and the value of $Z_{0}$ is to be estimated. The paper contents of Gaussian anamorphosis, varigram and structure analysis and Hermite polynomials. The application of these estimation methods to a deposit is described. The study carries out DK and ordinary kriging (OK) for a suit of 3D drill data of a multi-metal deposit, containing 63 drills and 2 kinds of metal, from which we can see DK is more perfect and easier. A discussion of the results from a practical point of view is also given |
| Starting Page | 4176 |
| Ending Page | 4179 |
| File Size | 403046 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780387422 |
| DOI | 10.1109/IGARSS.2004.1370055 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-09-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ores Polynomials Tin Educational institutions Information science Data mining Area measurement Pollution measurement Resource management Probability |
| Content Type | Text |
| Resource Type | Article |
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