Predicting geological and geomechanical rock properties using data analytics and multi-sensor core logging data

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A detailed modelling of geological and geotechnical rock mass properties is essential for a cost effective and safe mining operation. Despite improvements in almost every technological aspect of mine planning and design, lack of sufficient and consistently measured data for the development of a detailed geological and geotechnical model remains a challenge. A higher quality and quantity of geological and geotechnical data is required to model the spatial variation of rock mass properties and to lower uncertainties in mine planning and design. Core logging is a fundamental method in obtaining geological and geotechnical data. However, the manual core logging methods are subjective, time consuming and inconsistent, which can significantly reduce the reliability of the resulting geological and geotechnical models. To overcome these limitations, new rock measurement and analysis techniques that automate the collection and analysis of data are required. This research program aims to advance prediction of rock geological and geomechanical properties using data from a multi--sensor core logging system. This will be achieved through the collection of multi-variate rock physical, mechanical, mineralogical and structural properties from non--destructive tests, together with high resolution images of the core samples. The data will then be used to develop machine learning models to predict geological and geomechanical rock properties. Core samples from geotechnical boreholes will be logged using both the manual and the multi--sensor core logging system. The high- quality images of the core samples along with the multi--variate core logging data will be processed to develop a multi--modal dataset. The dataset will be used for predictive models of geological and geomechanical rock properties. Supervised machine learning techniques will be used to train predictive models using the multi--modal mixed data. The models will be employed to automatically: predict and classify lithological rock units and their mechanical properties; detect and characterize geometrical attributes of natural discontinuities using image analysis; predict shear behaviour of discontinuities using digital core logging data; classify geomechanical rock mass properties based on multi--parameter digital core logging data; and to investigate the influence of data quality and quantity on geotechnical models used for mine design. The predictive geomechanical models from the digital core logging system will be compared to the models developed based on manual core logging data. The results of the research are expected to improve rock characterization and classification; allow informed decision making in mine planning and optimization; reduce the time and cost associated with unpredicted ground conditions; and reduce the risk of mine excavation failure and its associated costs.