| Autor: |
Aida Zhexenbayeva, Nasser Madani, Philippe Renard, Julien Straubhaar |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Applied Computing and Geosciences, Vol 23, Iss , Pp 100177- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1974 |
| DOI: |
10.1016/j.acags.2024.100177 |
| Popis: |
Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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