Contribution of geostatistical modeling on simulating a geomechanical parameter (case study: GSI in Gol-Gohar open pit iron mine, Kerman, southeastern Iran)

Shademan, Marzieh; Farhadian, Hadi

doi:10.22077/jgm.2023.6696.1013

Contribution of geostatistical modeling on simulating a geomechanical parameter (case study: GSI in Gol-Gohar open pit iron mine, Kerman, southeastern Iran)

Document Type : Original Article

Authors

Marzieh Shademan ¹

Hadi Farhadian ²

¹ Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

² Department of Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran

10.22077/jgm.2023.6696.1013

Abstract

The successful implementation of the GSI system in numerous global construction projects has proven its capability in giving accurate estimations of the strength of diverse rock formations. The patterned arrangement of GSI values in open-pit mining corresponds to the occurrence of geological rifts. The utilization of geostatistical techniques is effective in comprehending the spread of regionalized factors in each designated research location. Because GSI is a non-additive variable, it may be more beneficial to simulate than estimate it. This is because simulation algorithms can provide results without smoothing. In this work, the theory of regionalized variables was used to analyze and interpret the spatial distribution of GSI values measured at the Gol-Gohar iron mine southeast of Kerman City in Kerman Province, southeastern Iran. Variographic techniques were used to understand and identify the regional behavior and distribution of GSI measurements. Moreover, it is feasible to model the GSI estimations for individual blocks on the pit wall and generate maps to decipher the performance of the localized factors. Such assessments can aid in devising strategies for enhancing the slope stability of pit walls to a maximum extent.

Keywords

Geological Strength Index

Gol-Gohar open pit mines

Non-additive variable

Sequential Gaussian Simulation