proposed methodology for utilising automated core logging ... · proposed methodology for utilising...

Proposed Methodology for Utilising Automated Core

Logging Technology to Extract Geotechnical Index Parameters

Cassady Harraden, Ron Berry, James Lett GeoMet 2016

June 15-16 Perth, WA

Introduction

Geotechnical assessment and modelling vital to mining

Rock mass

properties the

result of geologic

history of the ore

deposit

Mineability relies

on rock properties

and success of

geotechnical

models

Successful

geotechnical

assessment =

successful mining

= profit

Current Geotechnical Data Collection

Geotechnical models based on manually measured data

• Successful, but time-consuming

• Prone to inconsistencies

Corescan Technology

3D Laser Profiler

200 µm pixels

height resolution = 15 µm

Visible Light Camera (RGB)

60 µm pixels

Hyperspectral Scanner

0.5 mm pixels

450 to 2500 nm @ 4 nm

resolution (VNIR, SWIR)

Corescan Technology

RGB

Image

3D Laser

Image

RQD

Calcu-

lation

Mineral

Class

Map

Chlorite

wave-

length

Chlorite

abun-

dance

Epidote

wave-

length

Sericite

crystall-

inity

Opportunities for Geotechnical Data

• Continuous down hole core height data

• Opportunity to collect high volumes of consistent data

• Multi-data, integrated geometallurgical approach

Opportunities for Geotechnical Data

RQD = length core >10 cm

total length core run

Classification

Criteria

Rating

RQD 0 – 20

Fracture Spacing 0 – 20

Fracture

Condition

0 – 30

Groundwater

Condition

0 – 15

Intact Rock

Strength (UCS)

0 – 15

RQD Value Rock Quality

0 – 25% Very Poor

20 – 50% Poor

50 – 75% Fair

75 – 90% Good

90 – 100% Excellent

Q = RQD

Jn x

Jr

Ja x

Jw

SRF

Rock Mass Rating

(RMR)

Tunnelling Index

(Q-index)

RMR = all criteria

Classification

Criteria

Rating

RQD 0 – 100

Sets (Jn) 0 – 20

Roughness (Jr) 0 – 5

Alteration (Ja) 0 – 4

Water (Jw) 0 – 1

SRF 0 – 10

Rock Quality

Designation (RQD)

(Deere et al, 1967) (Bieniawski, 1989) (Barton, Lein and Lunde, 1974)

Proposed Methodology

1. Automatically

recognise fractures

RQD, RMR, and

Q-index

2. Determine

fracture orientation

α = 40

β = 160 Q-index α = 45

β = 170

3. Determine

fracture roughness

Q-index Jr = 4.5 Jr = 4

Jn = 1

RQD, RMR, and

Q-index RQD =

100%

RQD, RMR, and

Q-index


4. Filter mechanical

breaks

5. Enhance current

RQD

6. Determine

fracture sets

Q-index

RMR and Q-index

RQD and RMR 9.5 cm


7. Measure fracture

spacing

8. Determine

fracture condition

9. Method

verification RQD = 100%

Avg spacing = 9cm

Avg Jn = 1

Avg Jr = 4.5

Avg Ja = 5

vs

1. Fracture Recognition

Fractures represent discontinuities in cylindrical shape so:

Slope (degrees)

90°

0°

Flat

0.0 – 22.5°

22.5 – 67.5°

67.5 – 112.5°

112.5 – 157.5°

157.5 – 202.5°

202.5 – 247.5°

247.5 – 292.5°

292.5 – 337.5°

337.5 – 360.0°

Aspect (degrees)

Relative slope to

highlight fractures

Aspect filter to exclude

core curvature

Fracture

recognition

2. Fracture Orientation 64/101

59/112

55/227

71/226

76/245

54/226

80/105

True

Orientation

41/90

55/93

56/273

34/105

27/88 39/112

83/101

Apparent

Orientation

Extract x, y, and z

values of fracture

points

Fit plane to fracture

points (least squares

linear regression)

Account for drill hole

(2D linear

transformations)

3. Fracture Roughness

• Deviation of true

fracture points

from the

calculated flat

plane

• Develop

correlation

between Jr value

and sum of

residuals From Bieniawski, 1989

4. Filter Out Mechanical Breaks

3D Laser Profile RGB Core Image



Jr = 7.4 Ori = 45/325

Jr = 3.6 Ori = OR

Jr = 4 Ori = OR

5. Enhance Current RQD Calculations

Improved discrimination between

natural and mechanical breaks

6. Fracture Sets

Common joints sets

identified by statistical

grouping of orientations

7. Fracture Spacing

Distance between

fractures from down hole

coordinates

8. Fracture Condition and Alteration

• Mineralogy co-registered with 3D laser data

• Apply series of threshold values on mineral

type and abundance

clay

quartz

chlorite

no spectral signature

3D laser data from 10 cm interval of whole (uncut), HQ drill core

9. Methodology Verification

simulated model simulated model

Current Model Calculated Model

Compare

calculated

vs

currently

modelled

RMR and

Q-index

Proposed Corescan Outputs

RGB

Image

3D Laser

Image

RQD

Calcu-

lation

Mineral

Class

Map

Chlorite

wave-

length

Enhanced RQD and

automated, partial RMR and

Q-index calculations

RQD

Calc*

Partial

RMR

Calc*

Partial

Q-index

Calc*

RQ

D =

10

0%

RM

Rp =

92

QIn

de

xp =

11

4

*simulated

outputs

Key Points

Current geotechnical logging procedures manual, time-

consuming and inconsistent

Proposed methods would automatically extract key

parameters for RQD, RMR and Q-index

Methodology has the opportunity to provide rapid,

consistent, automated geotechnical assessment

Acknowledgements

• This research is being conducted by the ARC Research Hub for Transforming

the Mining Value Chain (project #IH130200004)

• Thanks to Anthony Harris (Newcrest Mining Ltd) and Neil Goodey (Corescan Pty Ltd)

• Special thanks to Maya Secheny, Chris Chester and Ann Winchester

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