Classifiers for modeling of mineral potential

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits. Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought

Mapping of Au anomalies in drainage sediments by multifractal modeling

This study aims to recognize and map Au anomalies from bulk leach extract able gold (BLEG; ignoring drainage sediment dilution) and catchment area weighted BLEG (CW-BLEG; considering drainage sediment dilution) data from an area (ca. 1500 km x 100 km) in the Usak-Esme region in Western Turkey. In this area, gold-bearing crystalline quartz veins, of probable orogenic type, containing up to 20 g/t Au occur in low-grade schists and marbles of the Karakaya Complex in the vicinity of Sogut are a [1]. The number-size (N-S) fractal model [2] was applied to classify the BLEG and CW-BLEG data separately in order to compare the resulting Au anomaly maps. Au anomaly maps were classified using the derived thresholds from the N-S model obtained for the BLEG data (0.467 ppb, 6.606 ppb and 9.12 ppb) and for the CW-BLEG data (1.819 ppb, 6.165 ppb and 128.825 ppb). Moderate to strong BLEG anomalies are concentrated in the west (Fig. 2A), whereas moderate CW-BLEG anomalies are present not only in the west but also in the central and eastern parts of the study area (Fig. 2B). Both maps show Au anomalies associated with known gold mineralization (e.g., Sogut, Mayislar) hosted by the Karakaya Complex in the west, but the CW-BLEG anomaly map additionally shows Au anomaly due to the Dumluca mineralization in the east. Therefore, although BLEG data are useful in geochemical exploration for gold, the effect of drainage sediment dilution should be considered and catchment area is a good spatial proxy to address that issue

Enhancement of Au anomalies: weight bulk leach extractable gold data with catchment

This work, in an area in western Turkey, aims to take the effect of drainage sediment dilution into consideration to enhance and recognize Au geochemical anomalies by using catchment area weighted-bulk leach extractable gold (CW-BLEG) data and S-A (spectrum-area) fractal modeling [1]. The results show moderate and strong CW-BLEG anomalies in the western, central and eastern parts of the study area (Fig. 1), where known gold mineralizations hosted in the middle-upper Triassic Karakaya Complex exist [2]. The S-A fractal model was implemented on the BLEG data as well, but this resulted in recognition of only some of the Au geochemical anomalies recognized using the CW-BLEG data. In particular, S-A modeling of the BLEG data does not enhance anomalies associated with the known gold mineralizations in the central and eastern parts of the study area. The results demonstrate that, in order to enhance and recognize significant Au anomalies in drainage sediments, the effect of drainage sediment dilution can be taken into account by using catchment area as a spatial proxy