An Image Analysis-Based Methodology for Chromite Exploration through Opto-Geometric Parameters; a Case Study in Faryab Area, SE of Iran

Abstract

Traditional methods of chromite exploration are mostly based on geophysical techniques and drilling operations. They are expensive and time-consuming. Furthermore, they suffer from several shortcomings such as lack of sufficient geophysical density contrast. In order to overcome these drawbacks, the current research work is carried out to introduce a novel, automatic and opto-geometric image analysis (OGIA) technique for extracting the structural properties of chromite minerals using polished thin sections prepared from outcrops. Several images are taken from polished thick sections through a reflected-light microscope equipped with a digital camera. The images are processed in filtering and segmentation steps to extract the worthwhile information of chromite minerals. The directional density of chromite minerals, as a textural property, is studied in different inclinations, and the main trend of chromite growth is identified. Microscopic inclination of chromite veins can be generalized for exploring the macroscopic layers of chromite buried under either the surface quaternary alluvium or overburden rocks. The performance of the OGIA methodology is tested in a real case study, where several exploratory boreholes are drilled. The results obtained show that the microscopic investigation outlines through image analysis are in good agreement with the results obtained from interpretation of boreholes. The OGIA method represents a reliable map of the absence or existence of chromite ore deposits in different horizontal surfaces. Directing the exploration investigations toward more susceptible zones (potentials) and preventing from wasting time and money are the major contributions of the OGIA methodology. It leads to make an optimal managerial and economical decision.

Keywords

• Opto-geometric parameters
• Chromite ore deposits
• Microscopic inclination
• Directional density
• Thin section image