Predicting gold targets using cokriging in SURFER 17
| Main Author: | Valls, Ricardo |
|---|---|
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2019
|
| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/mwzcwf5b5k |
| ctrlnum |
0.17632-mwzcwf5b5k.1 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc><creator>Valls, Ricardo</creator><title>Predicting gold targets using cokriging in SURFER 17</title><publisher>Mendeley</publisher><description>Golden Software Inc. included the method of cokriging in the newest version of SURFER 17. This has opened a new tool for interpreting geochemical data.
We can use cokriging in SURFER 17 to improve the quality of maps and to predict similar targets in nearby areas. We use cokriging when we want to process data from different datasets. One dataset is always smaller than the other.
Here, I first tasted the method with a hypothetical geochemical model combining a smaller dataset of FA gold results with a larger dataset of ICP-MS multi-elements. Later, I applied this method to real data from a soil sampling project in Mozambique. I tested a known mineralized target and also an extended area to predict gold targets. I also had the gold results for the extended area. They allowed me to confirm the effectiveness of cokriging in predicting the new targets.
There are many opportunities where we can apply cokriging as a prediction tool. One situation is when an initial sampling returned a group of interesting but isolated gold results. We can then use a cheaper method, like ICP-MS, to better understand the gold distribution in the area.</description><subject>Geology</subject><subject>Geochemistry</subject><subject>Geological Exploration</subject><type>Other:Dataset</type><identifier>10.17632/mwzcwf5b5k.1</identifier><rights>Creative Commons Attribution 4.0 International</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><relation>https:/data.mendeley.com/datasets/mwzcwf5b5k</relation><date>2019-05-02T01:10:24Z</date><recordID>0.17632-mwzcwf5b5k.1</recordID></dc>
|
| format |
Other:Dataset Other |
| author |
Valls, Ricardo |
| title |
Predicting gold targets using cokriging in SURFER 17 |
| publisher |
Mendeley |
| publishDate |
2019 |
| topic |
Geology Geochemistry Geological Exploration |
| url |
https:/data.mendeley.com/datasets/mwzcwf5b5k |
| contents |
Golden Software Inc. included the method of cokriging in the newest version of SURFER 17. This has opened a new tool for interpreting geochemical data.
We can use cokriging in SURFER 17 to improve the quality of maps and to predict similar targets in nearby areas. We use cokriging when we want to process data from different datasets. One dataset is always smaller than the other.
Here, I first tasted the method with a hypothetical geochemical model combining a smaller dataset of FA gold results with a larger dataset of ICP-MS multi-elements. Later, I applied this method to real data from a soil sampling project in Mozambique. I tested a known mineralized target and also an extended area to predict gold targets. I also had the gold results for the extended area. They allowed me to confirm the effectiveness of cokriging in predicting the new targets.
There are many opportunities where we can apply cokriging as a prediction tool. One situation is when an initial sampling returned a group of interesting but isolated gold results. We can then use a cheaper method, like ICP-MS, to better understand the gold distribution in the area. |
| id |
IOS7969.0.17632-mwzcwf5b5k.1 |
| institution |
Universitas Islam Indragiri |
| affiliation |
onesearch.perpusnas.go.id |
| institution_id |
804 |
| institution_type |
library:university library |
| library |
Teknologi Pangan UNISI |
| library_id |
2816 |
| collection |
Artikel mulono |
| repository_id |
7969 |
| city |
INDRAGIRI HILIR |
| province |
RIAU |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS7969 |
| first_indexed |
2020-04-08T08:10:52Z |
| last_indexed |
2020-04-08T08:10:52Z |
| recordtype |
dc |
| _version_ |
1686587387711324160 |
| score |
17.538404 |