1. ADVANCES IN DRILL RIG DEPLOYED RADARS Mr Tim Sindle, ARCO/CRC Mining Imaging Lab, The University of Sydney Dr Carina Kemp, Business Development Manager, GEOMOLE 11 th SAGA Biennial Conference and Exhibition, 16-18 September 2009 1 SAGA, September 2009

2. Outline SAGA, September 2009 2  Introduction  Method and Results  Survey Gear Minimisation  Analyzing Drill Deployed Data  Automatic Algorithm Development  Conclusions

3. Introduction – In-mine geophysics  Anticipate problems ahead of mining  Improve efficiency of mining operations  Bulky gear  Time consuming surveys cause delays in production 3 SAGA, September 2009 The Good The Bad No matter how good the results, if any technique cannot be easily and reliably implemented in the mining environment, it will not be used mainstream. No matter how good the results, if any technique cannot be easily and reliably implemented in the mining environment, it will not be used mainstream.

4. Introduction – Borehole Radar (BHR)?  Ground penetrating radar (GPR) in a drillhole  Reflections indicate a contrast in the electrical properties of the rock.  BHR provides high detailed continuous reflections from lithology contacts and structures. GeoMole BHR  10 – 124 MHz Bandwidth  Resolution: less than1m  Range: up to 50m or more (depending on rock type)  Probe diameter: 32 mm  BHR Profiling at ~10 m/min  Omnidirectional antenna 4 SAGA, September 2009

5. BHR then….  Survey trials of BHR showed very promising results, but the gear let us down.  50 kg optical fibre winch  20 kg push rods  10 kg probes 5 SAGA, September 2009

6. BHR Now - Minimal Gear  Radar Tool  1.6m  3kg  Non-conductive spacers  1.5m  2kg each  Drill attachment  PDA 6 SAGA, September 2009 RadarSpacers Drill Attachment + IQ + PDA

7. BHR Now – Drill rig deployed SAGA, September 2009 7 IQ Core barrel drill bit spacers

8. Drill Rig Deployed Borehole Radar - Pumpdown Radar Tool Spacers The radar tool continuously records data. The motion of the rods is discontinuous as the rods pulled and removed.

9. Depth Measurement (station) Measurement (Stationary) Winch Survey OTR Survey Stationary Moving Deployment Motion…

10. Winch SurveyOTR Survey Same 40m section of a horizontal borehole Radar Data… Stationary Moving

11. Raw Data  Aim: To understand the motion in order to work out how to recompress it.  Different motion for each type of drill-rig 11 SAGA, September 2009 Boart LM75 Diamond

12. Raw Data Recompressed Data Recompressing Radar Data..

13. Movement Log  Logging procedure tracks accurately the motion of the drill rig.  User records ‘MOVE’, ‘STOP’ and ‘ROD-CHANGE’ following the motion of the drill.  These events are time stamped and recorded for data processing 13 SAGA, September 2009 Accelerometers were installed in the radars to assist with movement logging

14. Time Log processed Data  Vulnerable to human error 14 SAGA, September 2009

15. Automatic Algorithm Development SAGA, September 2009 15 Using the accelerometer data for automatic processing:  Statistical deviation measurement  Fourier Spectrum Analysis  Velocity integration calculations

16. Statistical Processed Data Amplitude Traces 16 SAGA, September 2009 Standard Deviation Threshold

17. Accelerometer Processed Data  Suffers from random accelerometer events 17 SAGA, September 2009

18. Fourier Spectrum Analysis  Examine the power in various regions of motion  Difference observed between some moving and stopped traces by examining the higher frequency content.  However, drill vibrations cause wide band energy gains.  METHOD ABANDONED 18 SAGA, September 2009 * Stopped with drill shock Start of move Stopped Constant velocity move Frequency Spectrum

19. Velocity Processed Data  Noisy environment causes spurious accelerations and accurate velocity is hard to gather.  A high pass filter distributes the velocities around zero.  Then the mean representation of the velocity is calculated 19 SAGA, September 2009 Trace Amplitude Positive = Moving, Negative = Stopped

20. Velocity Processed Data  Copes well with the sharp drill shocks and vibrations as they often have equal positive and negative direction.  Captures the start and stop of the movement well.  Particularly violent jerks can cause a trace to be lost. 20 SAGA, September 2009

21. Comparison… SAGA, September 2009 21 Raw DataTime LogAccelerometerVelocity

22. Conclusions…  Drill deployed radars can be run with minimal disruption to normal work flow.  Using the time log alone can be vulnerable to human error  Yet all automated methods investigated so far are vulnerable to sharp spurious drill movements.  A combination of a time log together with statistical and velocity methods will result in smooth “winch quality” images being produced.  Development in this area continues 22 SAGA, September 2009

23. Conclusions SAGA, September 2009 23  The ultimate aim of a tool knowing its own position automatically is theoretically possible, but only within well defined constraints, and there will always be the unknown events on the drill rig that can cause inaccuracies.  The above progress makes it possible for quick data turnaround from survey to seamless integration of BHR data into mine planning packages, to enable day to day mining decisions to be made using such tools.

24. Acknowledgements… SAGA, September 2009 24  The authors would like to thank DeBeers Canada in particular Kevin Smith, for their ongoing feedback and use of the tool.  The funding contributions of ARCO, CRC Mining, and GeoMole are gratefully acknowledged.  Many thanks to the tireless work by Sydney University ARCO Lab members including; Andrew Bray, Steven Owens, and Phillip Manning.