Geo-Sol offers innovative approaches applicable in different stages of mineral exploration projects. The services can be offered to a wide range of commodities occurring as both hard and soft rock ore deposits.
Geo-Sol are Specialists in:
• Geological Mapping and Data Verification
The success of all exploration projects requires an in depth understanding of the lithology and structural geology of the area, so as to understand the controlling factors of mineralization. GeoSol offers a determined team of geologists that understands the importance of reliable field/geological mapping as a cost effective exploration tool.
• Structural Assessments and mapping for exploration targets.
Structural geology serves as one of the back bones for all exploration projects. This is because a lot of ore deposits occur in tectonically active localities. Thus, the understanding of geological structures plays an important role in the exploration and mining of ore deposits.
• Borehole planning, drilling management and supervision.
Drill Cores play a pivotal role in an exploration project. Drill Cores serve as underground insights of the underlying lithology. The quality of core data is highly dependent on the location at which the borehole is drilled. This means that careful planning and management of boreholes and their supposed drill locations plays an important role in the success of exploration projects.
• Core logging (diamond, percussion and RC)
Mineral Ores tend to occur underground. Drills cores serve as the only physical means of evidence for the existence and understanding of underlying ores. Drill cores are only as useful as the quality of data retrieved from them. A careful Study and analysis of drill cores allows for an effective interpretation of the underlying geology. Which then serves a guide for later stages of exploration.
• Core Sampling and Assaying
The collection of reliable, unbiased samples from a mineral prospect is essential for an accurate resource estimate and chemical analysis. Thus the sampling strategy must be carefully looked at, including other factors which may result in a false representation of the dataset as a whole.