Abstract
A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the method-ology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling param-eters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the result-ing models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89％to 96％.However,the simpler DI still yields fairly accurate results,with recognition rates 70％to 90％.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.
基金项目
European Union's Horizon 2020 research and innovation program(869379)
China Scholarship Council(202006370006)
NETL
NSTL
维普
万方数据