Slope Paper 238

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Proceedings, Slope Stability 2011: International Symposium on Rock Slope Stability in Open Pit Mining and Civil

Engineering, Vancouver, Canada (September 18-21, 2011)

Integrated Numerical Modelling and Insar Monitoring of a Slow Moving

Slope Instability at Bingham Canyon Mine

T.D. Styles AMC Consultants (UK) Ltd., Maidenhead, Berkshire, UKD. Stead Resource Geotechnics, Simon Fraser University, Burnaby, Canada

E. Eberhardt Geological Engineering, University of British Columbia, Vancouver, Canada

B. Rabus MDA Systems Ltd., Richmond, BC, Canada

M. Gaida Bingham Canyon Mine, Rio Tinto, Salt Lake City, UT, USA

J. Bloom Bingham Canyon Mine, Rio Tinto, Salt Lake City, UT, USA

Abstract

Remote sensing using satellite-based Interferometric Synthetic Aperture Radar (InSAR) provides a uniqueopportunity for monitoring slope deformation during the assessment of large open pit slope kinematics. This

case study presents the results of an integrated numerical modelling - monitoring investigation of a large open

pit slope at Bingham Canyon Mine, Utah, USA. Development of the pit slope geomechanical model involved the

use of a two-dimensional finite-discrete element code incorporating a discrete fracture network. The analysis of

mapping data from mine reports provided a solid foundation to understand the joint fabric and develop a

discrete fracture network, which was imported into the numerical model and explicitly simulated. This approach

proved successful in analysing slope deformation through combined brittle fracture and sliding along non-

daylighting shear zones, previously identified through subsurface monitoring. Development of kinematic release

along the shear zones was simulated by a combination of internal deformation, intact rock bridge fracture and

shearing within the assumed joint network. To simulate on-going slope deformation, the key driving factors had

to be considered with several simulations incorporating the potential seasonal effect of a perched aquifer. A

major outcome from the study was the development of techniques to interpret InSAR data and comparison of theresults with mine-based geodetic monitoring. This provides a valuable future constraint for geomechanical

modelling of large pit slopes directly incorporating the key aspects of the slope deformation mechanisms.

1 Introduction

Current practices in the mining of large open pits often involve push backs to increase achievable depths whilst

minimising the footprint. Slope behaviour within large open pits can be complex, with deformation mechanisms

involving a combination of both material (rock mass) and discontinuity control, with subsequent time dependent

motion as damage and creep processes occur within the slope. Advanced numerical codes can be used to

simulate slope instabilities, increasing our understanding of both rock mass strength and potential slope

deformation mechanisms. Such methods require integration with monitoring methods to correctly calibrate

models and provide an appropriate representation.Targeted monitoring and improvement of in-ground techniques were important discussion points during the

slope monitoring forum the 2007 Slope Stability Conference held in Perth (Slope Monitoring Forum, 2007).

Targeted monitoring using conventional prism-based techniques is difficult in early stages of slope deformationwhen the extent and the time dependent nature are undefined. Interferometric synthetic aperture radar (InSAR) is

a relatively new tool within the mining industry that is suitable for large-scale reconnaissance as well as detailed

monitoring; examples can be taken from the few industrial case studies where InSAR has been used for mine

slopes (Rabus et al., 2010; Herrera et al., 2010; Akcin et al., 2010), and the numerous applications to the

monitoring of landslides, subsidence due to coal mining, and subsidence resulting from aquifer abstraction


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(Cascini et al., 2010; Farina et al., 2007; Wegmuller et al., 2008; Toms et al., 2010). All demonstrate the

suitability of InSAR for detecting and monitoring motion over large areas at sub-millimetre accuracy. This large-

scale coverage can be used to provide an overview of slope deformations pit-wide, whilst retaining the accuracy

to target particular instabilities. Furthermore the precise spatial coverage that is provided is particularly useful

during the calibration of sophisticated numerical models.

This paper outlines an integrated modelling-monitoring study at Bingham Canyon Mine, south west of Salt Lake

City, Utah. The open pit is operated by Kennecott Utah Copper, Rio Tinto, and is currently the largest

excavation within the northern hemisphere, measuring approximately 3600 m wide and 900 m deep. The zoned

Cu-Au-Mo deposit is a subvolcanic porphyry which intruded quartzites and carbonates (Landtwig et al., 2010).

The monzonite orebody has a well-developed set of faults and fractures that are northeast striking (Babcock et

al., 1997), and therefore sub-parallel to the section of pit wall in which there is an ongoing slow deformation.

During spring 2002 tension cracks were discovered on an inactive haul road in the south pit wall (an area known

as the O cut), further cracking lower in the slope was observed during summer 2002. Consequently, additional

prism monitoring was installed in the area. The identified area of south wall deformation in the O cut was named

the O-Slide.

Figure 1. (a) Plan view of Bingham Canyon Mine, with annotation indicating the fault-bounded block that

defines the O-Slide; (b) inset is a geological cross section with Brooklyn Fault and Tunnel Fault as

mapped features, shear zones and no-name faults included as a worst case scenario model.


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Since identification of the slope deformation, a series of monitoring methods have been implemented, including

prisms, extensometers, inclinometers, microseismic monitoring, and ground-based radar. It is recognized that the

majority of the motion occurs within a fault bounded block, Figure 1. In addition, displacement rates are the

highest during the spring, most likely due to the recharge of an assumed perched aquifer within the upper part of

the slope. With the two faults, assumed to enable lateral release, a 2-D plane strain modelling approach can be

considered appropriate. As outlined in Section 1.2, a finite-element method (FEM)/discrete-element method

(DEM) code used is capable of simulating brittle fracture; such complex models however require carefulcalibration. For this case study, the InSAR coverage available within the project period spanned the period 2009

to 2010, in which time six InSAR scenes from RADARSAT-2 were successfully processed. As discussed in

Section 1.1, these provided snapshots of the motion of the whole pit slope, which in combination with the mine-

based geodetic data, provides an important constraint for the numerical modelling.

1.1 InSAR

To monitor slope motion using Interferometric Synthetic Aperture Radars (InSAR), several variables must be

considered. The basic principles of InSAR are summarised briefly below:

Synthetic aperture radar (SAR) is a ground imaging radar which synthesizes a large receiver by

coherently summing up echoes from a short, real antenna. Subsequently the image records the response

of the earths surface to radar microwaves emitted by the satellite platform.

Ascending and descending SAR images are captured when the satellite orbit passes north and south

respectively. For the example in this paper, descending scenes were appropriate for coverage of the

south and east pit slopes, whereas ascending scenes were more appropriate for the north and east pit

walls.

The SAR image records both the strength and the time delay up to an integer wavelength (i.e. the phase)

of the electromagnetic waves that were scattered by the ground surface.

The phase difference between two SAR images is analysed and contoured as fringes of equal phase

difference.

The two SAR images (interferogram) are generally taken at different times and can also be separated by

a spatial baseline, which causes the InSAR phase to be generally sensitive to motion and topography ofthe ground surface, as well as atmospheric delay.

Differential InSAR (DInSAR) is a method which crudely removes unwanted phase differences from

atmospheric disturbances and topography using a single or a couple of interferograms; this assumes that

the scattering properties of the ground surface remains undisturbed between the passes (Donavon, 2009).

Alternatively the Permanent Scatter technique (PS-InSAR) involves the selection of a number of pixels

within a stack of SAR (15+) images, which have again have scattering properties that do not change over

time. Subsequently the unwanted phase differences can be removed from the signal with high accuracy

(millimetre), using their contrasting spatio-temporal statistics (Rabus et al., 2009).

In general InSAR is suitable for slow moving instabilities that creep at a steady rate. The motion rate between

two adjacent pixels cannot be too large with respect to the temporal span of the interferogram and thewavelength of the microwaves at which the sensor operates. When the spatial gradient of the motion becomes

too large then the phase difference cannot be unwrapped unambiguously resulting in incoherence/de-correlation

in the interferogram.

The quality of InSAR is diminished by factors that change the scattering properties of the ground over time:

Vegetation

Atmosphere


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Ground conditions (snow cover)

Erosion/mining

All these factors introduce noise along with the signal of the moving ground surface. In addition topographycreates additional challenges, with unacceptable shadowing and layover in steep pit walls facing largely away

and towards the sensor, respectively (Rabus et al., 2009). Furthermore large open pits can also have a local

atmosphere within the pit with where seasonal and daily-scale changes of the bulk relative humidity causesignificant seasonal atmospheric phase error due to variations of water vapour.

With continued improvements in satellite technology and technical algorithms, such factors are becoming less

influential, with advanced statistical techniques to remove noise leaving an accurate indication of slope motion.

Notwithstanding, it must be recognised that specific wavelength InSAR (Table 1) is more appropriate in certain

conditions, for instance platforms that work over a longer wavelength (L-band) are more appropriate for:

Forested areas, as opposed to C-band which can become decorrelated due to seasonal growth

(Wegmuller, 2008; Farina et al., 2007); and

Areas of more rapid slope motion (Notti et al., 2010).

The case study presented within this paper, used stacks of RADARSAT-2 data with high (3 m) resolution. State-

of-the-art removal of the atmospheric and topographic error components was achieved with a novel permanent

scatter solution. Although RadarSat-2 captures an image every 24 days, it was not possible to develop a full

array of scenes both due to the presence and changes in winter snow cover. However, a linear relationship exists

between the changes in motion on the permanent scatters within each interferogram; subsequently mean

deformation rate was interpolated to provide snapshots of the motion rate during each scene.

1.2 Approach to numerical modelling

The software of choice within this paper is Elfen (Rockfield Software Ltd., 2010), a FEM/DEM code. The

dynamic fracture-based nature of the numerical code has been applied to numerous slope examples within

literature as summarized in Styles (2009), proving its capability to simulate stress-induced tensile fracturing,

damage and associated softening of a jointed rock mass. The method initially considers the rock mass as a

continuum using finite elements, but with explicit features, such as discontinuities embedded within the mesh.Owen et al. (2004) and Pine et al. (2007) provide details for the specific numerical routines within Elfen,

however in general terms rock mass strength is considered using a Mohr-Coulomb criteria combined with a

tensile cut-off (Rankine rotating crack), with a fracture energy approach that controls the subsequent fracture

propagation.

Importantly with any dynamic explicit procedure, there needs to be consideration of the duration over which

gravity loading or excavation processes occur over. Shock loading and subsequent unrealistic damage within the

models needs to be avoided by careful staging and monitoring of kinetic energy and unbalanced forces. With a

more complex geological model, this becomes more complex, as discussed in the following section.


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Table 1. Commercial radar satellites, operational history and capabilities (compiled from Rabus et al., 2009; HFarina et al., 2007, Wegmuller et al., 2008).

Satellite Full name Operational

period

Frequency

of orbit

Finest Spatial

Resolution*

(m)

B

m

J-ERS-1 Japanese Earth Resources

Satellite1992 - 1998 44 18

ALOS(Japanese) Advanced Land

Observation Satellite2006 - current 46 10

ERS-1 European Remote-Sensing

Satellite

1991- 2000 35 20

ERS-2 1995 - 2011 35 20

RADARSAT-1(Canadian Space Agency)

1995 - current 24 6

RADARSAT-2 2007 - current 24 1

EnviSat(European) Environmental

Satellite2001 - current 35 30

TerraSAR-X**

TanDEM**(German)

2008 - current

2010 - current

2.5 days

max.1

*Spatial resolution depends on the polarization of the acquisition method. In this case study the spotlight mode of RADAR

resolution of 3 m.

**Designed to fly together and collect data simultaneously, providing extreme accuracy and coverage, removing baseline adju


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2

Numerical model development

2.1 Rock mass strength and in-situ stress

The model presented in this paper is based on previous 2-D (UDEC) and 3-D discontinuum-based (3DEC)

modelling carried out by the mine and their consultants (Board and Gaida, 2010). Their studies formed thefoundation for the construction of the geometry, discontinuity network generation and rock mass strengthassumptions. Table 2 details the parameters necessary for the Mohr-Coulomb with Rankine tensile cut-off

criterion that was used to model brittle failure. In addition to the rock mass strength, the mechanical behaviour of

the major structures and embedded discontinuity network require definition, as detailed in Table 3.

Table 2. Rock mass strength properties.

Parameter MonzoniteClay

MonzoniteLimestone

Altered

LimestoneQuartzite

Young's Modulus, E, (GPa) 27.5 12 34 14 23.5

Poisson's Ratio, 0.2 0.12 0.25 0.23 0.27

Porosity, , (%) 1 50 30 20 10

Density, , (t/m3) 2.88 2.64 2.8 2.78 2.6

Densit with orosit t/m3 2.86 1.82 2.26 2.42 2.44

Bulk Modulus, K, (GPa) 15.3 5.2 22.6 8.65 17.0

Cohesion, c, (MPa) 1.8 0.59 2.9 1.1 0.65

Friction Angle, , () 37 35 40 32 35

Tensile Stren th MPa 0.2 0.35 0.87 0.35 0.27

Fracture Toughness, KIC*(MPa/m)

2.7 0.2 0.82 0.52 2.25

*Fracture toughness is used to estimate the fracture energy through an empirical relationship that is related to the intact

tensile strength and Youngs Modulus.

Table 3. Discontinuity properties.

Discrete Feature

Friction

Angle

()

Cohesion (kPa)

Normal

Penalty

(GPa)

Tangential

Penalty

(GPa)

Brooklyn Fault 16 3 1.5 0.08

Tunnel Fault 30 70 1.5 0.08

No Name aults 26 53 1.5 0.08

Shear Zones* 30 100 1 0.1

DFN and default properties* 30 0 0.2 0.02

*Friction and cohesion values were varied for specific model runs, as indicated within the text.


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As was shown in Figure 1b, within the modelled slope section, there is an assumed upper and lower shear zone.

These were interpreted from subsurface monitoring (inclinometers and time domain reflectometry), and it is

considered that they represent damaged zones that may have developed due to step-path failure along the joint

fabric. Step-path failure involves brittle fracture through intact rock bridges, allowing the interconnection of

discontinuities.

The estimated maximum horizontal stress is orientated approximately true north (003) and the plane of the 2D

model is at 318. Subsequently rotation of the in-situ stress was calculated to provide an in-plane numerical

model in-situ stress ratio, K, of 0.58 which is assumed for all models. Further work is recommended to ascertain

the influence of varied in-situ stress ratio assumptions.

As previously discussed, the recharge of an assumed perched aquifer (based on piezometer data), is suggested to

provide additional driving forces within the upper part of the slope. The current groundwater module in Elfen

influences only the shear stress acting along the discontinuities beneath the phreatic surface. Subsequently,

groundwater conditions were included to represent both the regional and perched groundwater surfaces,

imposing an elevated pore pressure on sections on the discontinuity network.

2.2 Discontinuity network

Small-scale structural data obtained from core logs, acoustic televiewer logs (ATV) and cell mapping indicate

three main joint sets (Fig. 2). Two of these form a fabric within the modelling section, that dips sub-parallel

(Joint Set 1), and slightly steeper (Joint Set 2), than the pit wall. Step-path fracture upon these dominant joint

sets may have formed the shear zones. Subsequently a number of discontinuity networks were developed to test

this hypothesis with the Elfen models.

Figure 2. Interpretation of joint sets that influence the behaviour within the section of the O-Slide model(orientated to Mine North).

Cell mapping carried out by the mine consultants provided a stochastic spacing and trace lengths data set.

Several discontinuity networks were generated using the automatic joint generator in Phase2(Rocscience, 2011),

and then imported into Elfen. Primary simulations indicated that the pocket of skarn within the modelled section

deforms in a more brittle manner than the monzonite (Fig. 3) prompting a review of the model stage process to


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ensure dynamic behaviour within the simulation is appropriate. Simulations using this discontinuity network

indicated a greater likelihood of joint slip along Joint Set 1 as opposed to Joint Set 2, even though Joint Set 2 has

a greater dip angle. As a result, two additional discontinuity networks were developed. This allowed

investigation of the suitability of smaller scaling values to provide an appropriate pathway for step-path failure

within the slope (Fig. 4).

Figure 3. Elfen model using the discontinuity network generated by Phase2.

In order to implement the above discontinuity networks within the Elfen model, considerable effort was required

to optimize the mesh geometry in relation to the internal lithological boundaries. With discontinuity networks

that include a regular spacing and trace length, mesh optimization is less demanding than with more stochastic

discontinuity networks. A similar scaled but alternative representation of a discontinuity network was also

modelled; these were based on sections that were developed by Call & Nicholas Inc. (CNI), to study the likely

angle of step-path failure. Improved internal deformation and shearing is achieved using a more stochastic

representation of the discontinuity network, however with a more complex discontinuity network, mesh

optimization process is more demanding. Subsequently the discontinuity network was used within a modelwhich explicitly included the shear zones (Fig. 5).


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Figure 4. Model results from simulations with different scaled discontinuity networks, in which only Joint Set

1 is considered, demonstrating step-path failure within the vicinity of the shear zones.


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Figure 5. Elfen model using scaled sections of the discontinuity network generated by CNI (Personal

Communication, 2010), embedded at the terminations of potential, but explicitly modelled shear

zones, fracture extension within the discontinuity network is presented.

3 Analysis results

3.1

Review of monitoring data

To provide clear representation of the InSAR data, an approach was developed whereby line of sight (LOS)

motion maps could be imported into ArcGIS (Esri, 2011); this involved conversion of a GeoTIFF format into a

raster file that had an attribute table. Subsequently a series of pit wide images were generated (Fig. 6). In

addition to the InSAR data, a review of the mine geodetic data was performed. Of the 37 prisms located on the

O-Slide, a subset was looked at in more detail (Fig. 6). In particular, the full data set, from 2002 2010, was

analysed for one prism located within the middle section of the slide, O-66.

Within the early prism monitoring system data, significant noise can be observed when viewing the motion over

a short time frame. To minimise this, motion rates were filtered and average rate used to interpolate cumulative

displacement. Subsequently average seasonal variation was plotted, along with the InSAR data which was

converted from LOS to fall-line displacement using a photogrammetric digital elevation model (Fig. 7).


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Figure 6. (a) InSAR scene of the whole open pit from October to November 2009, (b) enlarged image with the

Giant Chief and Copper Centre faults outlined in grey and annotation of selected prisms.

Figure 7. Seasonal rates of motion around Prism O-66.

3.2 Numerical simulation of the O-Slide displacement

As indicated in Section 2.2, a more extensive and complex discontinuity network allows an increased degree of

internal deformation and subsequent kinematic release. However, within the available time frame, a uniformdiscontinuity network could only be considered (Fig. 4). Cumulative displacement within such Elfen models

proved insufficient, even after cycling of the perched groundwater and inclusion of explicitly modelled shear

zones, Figure 8a. Higher rates of motion were achieved however within models that used a more irregular

stochastic representation of Joint Set 1, previously presented in Figure 5. Note, both models include reduced

frictional and cohesive strength on the embedded shear planes (10 and 0 kPa respectively), and the discontinuity

network (20). As discussed in the following section, further work is required to refine the strength estimation

and kinematics within the fracture network model, in respect of the low shear strength values stated above,

which are below the lower bounds of realistic values.


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Figure 8. Motion within Elfen model with a regular discontinuity network and embedded shear zones,

(b) nodal displacement in a location close to Prism O-66, with a comparison of motion for a

model within a more stochastic discontinuity network (Fig. 9a).

In numerical models of the O-Slide, the constraint of spatial motion within the model is imperative in order to

capture the correct slope deformation mechanism. Considering the areal coverage and high degree of accuracy

the InSAR data is particularly useful for this purpose; primary comparisons with numerical model resultsimprove confidence in the model results indicating the maximum slope motion to be within the lower third of the

slope (Fig. 9). This data is also supported by the existing slope monitoring data.

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Conclusions

The development of a 2-D discontinuity based Elfen model, with intact rock fracture capability, has provided an

insight into the deformation behaviour of a large slope. Further work is required including an improved

representation of the structural fabric within the pit wall, and calibration of the progress of intact rock fracture

within Elfen model by using the inclinometer and Time Domain Reflectometry (TDR) data. A brief review of

TDR data shows that shearing occurred early on in the InSAR monitoring period (Spring/Summer 2009) with

holes sheared within the base of the slope and at moderate depths (23 125 m). In contrast shearing at the top of

the slope occurred later (Fall/Winter 20092010), and at shallower depths (15

41 m).

The InSAR data suggests that motion on the O-Slide is fault bounded. Temporal changes in the InSAR data have

been investigated and indicate seasonal rates of motion comparable with selected ground-truth prism data from

the mine; consequently the highly accurate spatial InSAR data can be used to constrain the 2D numerical model.

Initial comparisons are encouraging although further work is necessary, to relate the rates of motion detected by

the InSAR, to the numerical modelling results and further develop the numerical model.


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Figure 9. (a) Displacement in x-axis of Elfen model (b) linking spatial changes in motion on O-Slide to

InSAR data, for a particular scene (Oct.-Nov. 2009).

Numerical modelling showed that a complex discontinuity network is required to enable kinematic release

through internal deformation, intact rock fracture and shearing. Within the current model, this was possible by

embedding the discontinuity networks at the terminations of the major shear zones. Also in this case reducedshear strength on the embedded shear planes, provided a mechanism by which elevated failure rates could be

achieved.

The results presented are preliminary in nature. Further study is required to improve the method of shear strength

reduction, refine the model mesh, and better represent the DFN networks. The results obtained however, clearly

demonstrate the significant potential in the combined use of InSAR and numerical modelling to improve our

understanding of the kinematics of slope deformation mechanisms in large open pits.

5 Acknowledgements

The research outlined in this paper was funded through NSERC Discovery, NSERC CRD and Canadian Space

Agency grants in addition to an SFU Endowment fund. Thanks are extended to individuals who were major

contributors to the project, including Christian Nadeau and Jason Eppler, and Andre van As, Alex Vyazmensky,Zip Zavodni and Martyn Robotham from Rio Tinto.

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Slope Paper 238