Exploration projects are inherently risky, and the success of exploration companies depends first and foremost on their ability to reduce risk consistently and continuously around their projects. This is vital because only a small percentage of exploration properties become mines, and thus time, capital and efforts are wasted on unfeasible projects. To perform their risk management function more effectively, executives in the mining industry must rely heavily on data science. Most decisions, from project selection to choosing targets for drilling on site should not be random. They should be evidence-based to be effective. Tarku Resources has mastered the art and science of using data to generate high potential exploration projects as well as guide its exploration efforts. The results thus far are remarkable.

Using Data to Generate High Potential Projects

Tarku currently owns promising projects in Arizona in the United States, and in Quebec, Canada. Governmental institutions and other entities in the industry provide access to extensive and comprehensive datasets about the geology of different locations. Tarku’s geologists and other experts have assessed the data and found that the potential rewards outweigh the costs in its 3As projects (Apollo, Admiral, Atlas) in Mattagami, Canada, and its Silver Strike project in Arizona, and invested in them. The result is that the company’s exploration projects show high grades of various minerals and include gold, silver, copper, lead and zinc.

Relaying Advanced Technology and Scientific Expertise to Generate Drilling Targets

  • 552.5 g/t Ag Eq, including 482 g/t Ag and 1% lead-zinc combined
  • 407.3 g/t Ag Eq, including 353 g/t Ag and 0.7% lead-zinc combined
  • 358.2 g/t Ag Eq, including 280 g/t Ag and 1.6% lead-zinc combined
  • 354.3 g/t Ag Eq, including 187 g/t Ag and 2.3% lead-zinc combined
  • 301.1 g/t Ag Eq, including 244 g/t Ag and 1.1% lead-zinc combined

Figure 1: Highlights from recent results on the Silver Strike project

Earlier in 2022, Tarku initiated a cooperation with GoldSpot Discoveries Corp, which has internationally renowned experts in geoscience and a proprietary technology, to generate drilling targets on its Arizona project. The approach, which relies on machine learning to improve the mineral discovery process has already commenced, and the Silver Strike project is progressing fast. Earlier in September, Tarku announced more high-grade results as shown in figure 1.

According to the company, GoldSpot’s geophysical services division utilizes the M-PASS system, a triaxial magnetic gradient/VLF platform, with a 2048-channel radiometric sensor that includes a high-resolution camera capable of producing four-band imagery. The combination of these systems onto a single helicopter allows for increased data captured through a single mobilization and flight creating a significantly cost-effective solution for multi-parameter data.

More recently, Tarku also announced that nine high-priority targets, totaling 5.35km in length, have been generated ahead of the maiden drill program at the Apollo Gold Project, following the completion and interpretation of the deep-penetrating and high-resolution IP survey. Apollo is one of the 3As project in Canada, and Tarku has recently received a private placement to initiate drilling on it.

Final Thoughts

Using the tools and techniques of data science to help advance exploration projects is not new. However, some players in the industry have improved the process significantly. The industry still has challenges with identifying which data to gather and analyze, and this is where the role of geoscience becomes highlighted. To interpret the data and build models to understand the mineralization zones, the expertise of geologists is invaluable. The data is abundantly available but leveraging this data and turning it into high potential and successful projects requires deep relevant knowledge and skills. Tarku has managed to do this, and hopefully over the longer term this will prove that Tarku has significantly improved capital allocation decisions, and improved exploration efficiency.

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