Document Type: Case Study

Authors

Applied Research and Independence Center of Khatamolanbia Company, Tehran, Iran

Abstract

The Dolatabad area located in SE Iran is a well-endowed terrain owning several chromite mineralized zones. These chromite ore bodies are all hosted in a colored mélange complex zone comprising harzburgite, dunite, and pyroxenite. These deposits are irregular in shape, and are distributed as small lenses along colored mélange zones. The area has a great potential for discovering further chromite resources. Therefore, the current work endeavors to delineate the favorable zones of podiform chromite mineralization to focus on the detailed exploration surveys. In order to achieve this goal, the machine learning random forests algorithm was adapted to integrate the footprints of mineralization in various exploration datasets. The genetic characteristics of podiform chromite deposits were used to define the exploration criteria. These defined criteria were then translated to a set of exploration evidence layers. The competent exploration evidence layers, i.e. those with remarkable positive spatial associations with mineralization, were then recognized using distance distribution analysis. Respecting the location of known chromite mineralizations and competent exploration evidence layers, a predictive random forests model was trained and then applied to predict the favorable zones of chromite prospectivity. The delineated targets were found to occupy 19% of the studied area, in which all the known chromite mineralizations were delimited. Consequently, it is worthy to follow up the detailed exploration surveys within the delineated zones.

Keywords