The application of threshold gold values based on geochemical exploration in complex regolith terrains: A case study at Lawra Greenstone Belt of Northwest Ghana
Interpreting surface gold geochemical data to define anomalous sites in complex regolith terrains is generally very challenging. Working around the challenges in geochemical data interpretation to avoid defining ‘positive false anomalies’ as ‘positive true anomalies’ require the application of multiple thresholds that account for the regolith environment in the analysis. 367 regolith samples were collected from four different regolith regimes namely-ferruginous (F), relict (R), erosional (E) and depositional (D) regolith respectively. Q-Q Probability method was applied on the gold data obtained from the regolith regimes to determine populations in the data distributions to detect anomalies. The first natural breaks from probability Q-Q plots defined the thresholds for the respective ferrugninous, relict erosional and depositional FRED regimes. These were 40 ppb for F, 30 ppb for R, 70 ppb for E and 25 ppb for D. The gold geochemical responses in the different regolith regimes were extracted and normalized using the different thresholds separately in the 6000 historical soil data. An anomaly maps showing enrichment factors of gold were plotted. The gold concentrations in the regolith were reclassified into the four regolith regimes. Gold thresholds were then estimated for each of the regolith regimes as the regolith-landform modification at each regolith will be different. Each estimated threshold value is likely to define gold anomaly in the regolith regime it was calculated from. This will provide equal weight of all gold anomalies relating to bedrock mineralization if thresholds-based-on regolith environment is applied on geochemical data during interpretation. A single threshold of 34 that had no unit was later defined from a combined normalized data obtained for all the regimes. Using an Indictor Krigging in GIS environment and applying 34 as the threshold, an enhanced prospectivity map outlining and prioritizing anomalies at a scale of ‘0’ to ‘1’, where ‘1’ is the most likely anomaly to host mineralization and ‘0’ lack of mineralization area were defined. This data interpretation approach using different thresholds based on regolith environments confirmed some defined anomalies that were drill-tested to host potential bedrock mineralization. The use of the technique is practically feasible and its application is recommended in complex regolith environments in the savannah of northern Ghana and other similar terrains dominated by complicated regolith in the West African Sub-Saharan Region and globally.
Keywords: Regolith, Geochemistry, Gold, Exploration, Anomaly, Mineralization
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