Virtual Reality Scientific Visualisation - A Solution for Big Data Analysis of the Block Cave Mining System

Transcription

1 Virtual Reality Scientific Visualisation - A Solution for Big Data Analysis of the Block Cave Mining System James Tibbett, Fidelis Suorineni, Bruce Hebblewhite and Alex Colebourn

2 What is Block Caving? Block caving is an underground mass mining method Large, unsupported excavation Stress acts on the rock material, natural features Lack of access to observe the failure process What is Big Data? All Big Data was not created equal Evolving definition Volume Velocity Variety Big Data and scientific visualisation

3 Example Block Cave Mining System Seismic monitoring, nd... Drawbell formation Undercut blast Existing mine geometry Surface(s) Multiple, multi-dimensional factors within the BCMS Various formats Preconditioning Geological block model Geotechnical block model Cave back estimations Big Data situation Fixed X, Y, Z, rock unit, grade Fixed X, Y, Z, RMR, Q Weekly Surface Data integration difficulties Holistic analysis needed Open hole monitoring Cave draw Daily Geological fault/ contact interpretation Numerical stress modelling Quality of data in, quality of knowledge out, tonnes Surface(s) X, Y, Z, nd... Data filtering Data integration Investigation and analysis BCMS optimisation

4 Block Cave Mining System Visualiser Built for the AVIE Sense of immersion Collaboration Spatial and temporal context Playback

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6 What will the future hold? More data Volume More frequent measurements Velocity New sensors/technology/mining conditions Variety The need for more efficient, optimised operations The future of mining is smart mining!

7 Equipment Becoming Mobile Sensor Networks McGagh J Rio Tinto Mine of the Future [Online]. Internet of Things World Forum: Chicago: Rio Tinto. Available: [Accessed June 2015].

8 Image Based Ore Quality Detection Images courtesy of Illuminate:

9 Possible Future BCMS Big Data Situation Seismic monitoring Drawbell formation Undercut blast Existing mine geometry Rock support load monitoring Over-size/hang-up monitoring Fragmentation assessment Per bucket, nd... Surface(s), N, m 2, m 2 Preconditioning Geological block model Geotechnical block model Cave back estimations Geological fault/ contact interpretation Drone drive deformation monitoring Seismic tomography velocity model, radius, m 2, dip, dip direction X, Y, Z, rock unit, grade X, Y, Z, RMR, Q Surface Surface(s), m, m/s Open hole monitoring Cave draw Geological fault/ contact interpretation Numerical stress modelling Drone cave back mapping/monitoring Pillar load monitoring Brow wear monitoring Per bucket, tonnes Surface(s) X, Y, Z, nd..., structure, mineralisation, fragmentation, N, m Data filtering Data integration Investigation and analysis BCMS optimisation

10 Virtual Reality and Artificial Intelligence Real-time behavioural data integration and analysis Machine learning enables an algorithm to evolve Better predict future data patterns Heuristic approach to learning when fuzzy data encountered Work with human intelligence Automated, self-optimising pattern recognition with human control Fuzzy Logic, Artificial Neural Networks and Genetic Algorithms

11 Virtual Reality and Machine Learning Human and machine collaboration

12 Conclusions The BCMS is complex, mines are generating more data than they can analyse, optimisation comes from understanding all of the data VRSV is an effective tool at integrating the Big Data from current BCMS Future mining is smart mining with more Big Data Machine learning can assist humans with automated, self-optimised pattern recognition Turning data into value

13 Thank You for Listening Questions?