Improving short-term grade block models: alternative for correcting soft data
Abstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on g...
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| Format: | Article |
| Language: | English |
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Fundação Gorceix
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| Series: | REM: International Engineering Journal |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2018000100117&lng=en&tlng=en |
| _version_ | 1811305077835563008 |
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| author | Cristina da Paixão Araújo João Felipe Coimbra Leite Costa Vanessa Cerqueira Koppe |
| author_facet | Cristina da Paixão Araújo João Felipe Coimbra Leite Costa Vanessa Cerqueira Koppe |
| author_sort | Cristina da Paixão Araújo |
| collection | DOAJ |
| description | Abstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on grade estimation and propose a method of correcting the imprecision and bias in the soft data. In addition, this paper evaluates the benefits of using soft data in mining planning. These concepts are illustrated via a gold mine case study, where two different data types are presented. The study used Au grades collected via diamond drilling (hard data) and channels (soft data). Four methodologies were considered for estimation of the Au grades of each block to be mined: ordinary kriging with hard and soft data pooled without considering differences in data quality; ordinary kriging with only hard data; standardized ordinary kriging with pooled hard and soft data; and standardized, ordinary cokriging. The results show that even biased samples collected using poor sampling protocols improve the estimates more than a limited number of precise and unbiased samples. A welldesigned estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model. |
| first_indexed | 2024-04-13T08:18:49Z |
| format | Article |
| id | doaj.art-46d83a231667408ebd34ced46acad7d9 |
| institution | Directory Open Access Journal |
| issn | 2448-167X |
| language | English |
| last_indexed | 2024-04-13T08:18:49Z |
| publisher | Fundação Gorceix |
| record_format | Article |
| series | REM: International Engineering Journal |
| spelling | doaj.art-46d83a231667408ebd34ced46acad7d92022-12-22T02:54:42ZengFundação GorceixREM: International Engineering Journal2448-167X71111712210.1590/0370-44672016710007S2448-167X2018000100117Improving short-term grade block models: alternative for correcting soft dataCristina da Paixão AraújoJoão Felipe Coimbra Leite CostaVanessa Cerqueira KoppeAbstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on grade estimation and propose a method of correcting the imprecision and bias in the soft data. In addition, this paper evaluates the benefits of using soft data in mining planning. These concepts are illustrated via a gold mine case study, where two different data types are presented. The study used Au grades collected via diamond drilling (hard data) and channels (soft data). Four methodologies were considered for estimation of the Au grades of each block to be mined: ordinary kriging with hard and soft data pooled without considering differences in data quality; ordinary kriging with only hard data; standardized ordinary kriging with pooled hard and soft data; and standardized, ordinary cokriging. The results show that even biased samples collected using poor sampling protocols improve the estimates more than a limited number of precise and unbiased samples. A welldesigned estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2018000100117&lng=en&tlng=enbiased samplesgrade estimateskrigingcokrigingsampling error |
| spellingShingle | Cristina da Paixão Araújo João Felipe Coimbra Leite Costa Vanessa Cerqueira Koppe Improving short-term grade block models: alternative for correcting soft data REM: International Engineering Journal biased samples grade estimates kriging cokriging sampling error |
| title | Improving short-term grade block models: alternative for correcting soft data |
| title_full | Improving short-term grade block models: alternative for correcting soft data |
| title_fullStr | Improving short-term grade block models: alternative for correcting soft data |
| title_full_unstemmed | Improving short-term grade block models: alternative for correcting soft data |
| title_short | Improving short-term grade block models: alternative for correcting soft data |
| title_sort | improving short term grade block models alternative for correcting soft data |
| topic | biased samples grade estimates kriging cokriging sampling error |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2018000100117&lng=en&tlng=en |
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