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SAGA, September 2009 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 11th SAGA Biennial Conference and Exhibition, 16-18 September 2009 Outline 2 Introduction Method and Results Survey Gear Minimisation Analyzing Drill Deployed Data Automatic Algorithm Development Conclusions SAGA, September 2009 Introduction – In-mine geophysics 3 The Good Anticipate problems ahead of mining Improve efficiency of mining operations The Bad Bulky gear Time consuming surveys cause delays in production 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. SAGA, September 2009 Introduction – Borehole Radar (BHR)? 4 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 SAGA, September 2009 BHR then…. 5 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 SAGA, September 2009 BHR Now - Minimal Gear 6 Radar Tool 1.6m 3kg each Drill attachment PDA + IQ 2kg + Non-conductive spacers 1.5m PDA Radar SAGA, September 2009 Spacers Drill Attachment BHR Now – Drill rig deployed 7 drill bit Core barrel spacers SAGA, September 2009 IQ Drill Rig Deployed Borehole Radar The radar tool continuously records data. The motion of the rods is discontinuous as the rods pulled and removed. - Pumpdown Spacers Radar Tool Deployment Motion… OTR Survey Moving Depth Measurement (Stationary) Measurement (station) Winch Survey Moving Stationary Depth Radar Data… Winch Survey OTR Survey Moving Same 40m section of a horizontal borehole Stationary Raw Data 11 Aim: To understand the motion in order to work out how to recompress it. Different motion for each type of drill-rig Boart LM75 Diamond SAGA, September 2009 Recompressing Radar Data.. Raw Data Recompressed Data Movement Log Amplitude Logging procedure tracks accurately the motion of the drill rig. User records ‘MOVE’, R R ‘STOP’ and ‘ROD-CHANGE’ following the motion of M S M SM S M SM the drill. Trace Number These events are time stamped and recorded Accelerometers were installed in for data processing the radars to assist with movement logging SAGA, September 2009 Accelerometer Data Radar Data 13 Time Log processed Data 14 Vulnerable to human error SAGA, September 2009 Automatic Algorithm Development 15 Using the accelerometer data for automatic processing: Statistical deviation measurement Fourier Spectrum Analysis Velocity integration calculations SAGA, September 2009 Statistical Processed Data 16 0.2 0.1 Standard Deviation Threshold 0 Amplitude -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 700 705 710 715 720 Traces 725 SAGA, September 2009 730 735 740 745 Accelerometer Processed Data 17 Suffers from random accelerometer events SAGA, September 2009 Fourier Spectrum Analysis 18 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 Frequency Spectrum Single-Sided Amplitude Spectrum of y(t) 0.04 * 0.035 Stopped with drill shock Start of move Stopped Constant velocity move 0.03 0.025 Amplitude 0.02 0.015 0.01 0.005 0 0 5 10 15 SAGA, September 2009 20 25 Frequency (Hz) 30 35 40 Velocity Processed Data 19 Noisy environment causes spurious accelerations and accurate velocity is hard to gather. acceleration velocity Positive = Moving, Negative = Stopped A high pass filter distributes the velocities around zero. Then the mean representation of the velocity is calculated Amplitude 1 0.5 0 -0.5 -1 700 705 710 715 720 725 Trace SAGA, September 2009 730 735 740 745 750 Velocity Processed Data 20 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. SAGA, September 2009 Comparison… 21 Accelerometer Raw Time Velocity Data Log SAGA, September 2009 Conclusions… 22 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 SAGA, September 2009 Conclusions 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. SAGA, September 2009 Acknowledgements… 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. SAGA, September 2009