Predictive Index for slope instabilities in open pit mining

TL;DR

This paper introduces static and dynamic models using remote sensing data and data mining to predict slope instabilities in open pit mines, enabling early risk detection and better planning.

Contribution

It presents novel static and dynamic models that integrate mathematical modeling and data mining for slope stability prediction in open pit mining.

Findings

Static model predicts slow movements months or years in advance.

Dynamic model forecasts collapse risks within days.

Methodology supports proactive planning and risk mitigation.

Abstract

In this paper we study the stability and deformation of structures, in particular the wall of an open pit mine is studied by using information obtained from a variety of remote sensors and some extra data, with a novelty approach considering the use of mathematical models and data mining techniques. In particular we present two models to help the study the slope stability of pit and the possible occurrence of movements. Primarily we present an static model for slow movements, which will help us identify areas of possible risks areas with time horizons of several months or years, depends on the available information, before the wall start moving, and secondly a dynamic short-term model, which help us to determine risks of collapse zones with several days in advance. We remark that this methodology can be a powerful tool to plain future actions in order to simulate possible scenarios considering the production plans.