(Special session) Resource exploration and big-data processing［Wed. Sep 13, 2023 AM Room-1］

[2101-04-04] [Student presentation: Doctoral course] Geostatistical integration of geologic, ore grade, and rock property data for geometallurgical approach to optimal planning of mine development

○Sylvie Rindraniaina RAHARISOLONJANAHARY¹, Katsuaki Koike¹, Mohamad Nur Heriawan², Vitor Ribeiro de Sá¹ (1. Kyoto University, 2. Bandung Institute of Technology)

Chairperson: Taiki Kubo (Kyoto University), Tatsu Kuwatani (JAMSTEC), Akihisa Kizaki (Akita University)

Keywords:Tin deposit, tin grade, RQD, spatial modeling, kriging

Geometallurgy studies are of vital importance in the mining process, revolutionizing the industry with their innovative approach. By integrating geology and metallurgy, these studies play a crucial role in enhancing efficiency and sustainability. Recognizing the spatial variability of ore deposits, geometallurgy provides a comprehensive understanding of their geological and metallurgical characteristics, enabling mining companies to optimize extraction methods. Through the identification of valuable mineralogical and textural features, processing plants can be designed to maximize recovery rates while minimizing environmental impact. Geometallurgy brings innovation by using advanced analytical techniques and computer modeling to quantify ore variability and develop optimal mining strategies. A compelling study case that showcases the significance of geometallurgy is the exploration and extraction of tin deposits. Tin, a critical element in various industries, presents complex mineralogical challenges. As a preliminary study of geometallugy, a tin deposit in Indonesia is selected as a test field, and the spatial distributions of tin grade and rock quality distribution (RQD) are modeled by geostatistics. By accurately modeling these parameters, mining engineers can tailor mining and processing methods to optimize tin recovery and resource utilization while reducing energy and water consumption. Furthermore, this approach can increase the accuracy of spatial variability and refine the geometallurgical model with additional information.