51_williams.pdf

Slope Stability 2013 P.M. Dight (ed) 2013 Australian Centre for Geomechanics, Perth, ISBN 978-0-9870937-5-2

Slope Stability 2013, Brisbane, Australia 763

J.G. Williams Department of Geography, Durham University, UK

N.J. Rosser Department of Geography, Durham University, UK

A. Afana Department of Geography, Durham University, UK; and 3D Laser Mapping Ltd, UK

G. Hunter 3D Laser Mapping Ltd, UK

R.J. Hardy Department of Geography, Durham University, UK

The reliable monitoring of slope deformation is a significant parameter for mitigating landslide damages, including business disruption and danger to workers. Despite this, remote sensing of surface deformation used to interpret failure mechanisms at the shear zone remains limited by factors such as the resolution and viewing angle of monitoring. Here we present an analysis of data captured using a new generation of full waveform terrestrial laser scanners (FW-TLS), which offers potential gains for near real-time rock slope monitoring. This approach, having evolved from recent advances in airborne LiDAR, resolves the structure of the reflected laser signal (the waveform) from which a series of attributes of the surface character, geometry and deformation are extracted.

The influence of target geometry, analogous to a deforming rock face, on the reflected waveform is interpreted from a set of controlled condition datasets. The analysis highlights the sensitivity of the maximum amplitude, relative to other parameters of the waveform, to changes in target geometry. We conclude by considering the implications for slope deformation monitoring of this new approach.

The temporal evolution of instability within rock masses, especially those of steep slopes excavated by surface mining in open pits, has been characterised in a number of studies (for example, Zavodni, 2000; Eberhardt et al., 2004; Kemeny, 2005). A simple observation is that slope failure is preceded by slope deformation; however, the rate-dependency of this deformation has also been applied in final failure-time predictions (both successfully and unsuccessfully) within a number of open pit mines. Zvelebil (1984), Suwa (1991), Hungr and Kent (1995), and Rose and Hungr (2007) extrapolated surficial strain-rates to the point in time where the reciprocal of strain-rate approaches zero, commonly termed the Saito method, in order to predict final failure. Other studies have also monitored derivatives of strain accumulation in deforming slopes, such as spallation (rockfalls) (Rosser et al., 2007), micro-seismic activity (Amitrano, 2005) and tension crack opening.

Viewing deformation as a precursor to final failure underlines a clear need to monitor deforming slopes but at present a priori analysis remains site-specific, costly and unreliable. Furthermore, there remains uncertainty regarding the detailed mechanisms of failure development, and their manifestation as surface strain. The reasons behind this include:

Spatial resolution of monitoring the scale of precursory deformation is often less than or beyond 1.the spatial resolution of monitoring. Critical levels of pre-failure strain, believed to be necessary to enable final catastrophic failure, have been shown to be only ~3% of the final shear surface length

Can full waveform technology enhance the use of terrestrial laser scanning to monitor J.G. Williams et al. rock slope deformation?

764 Slope Stability 2013, Brisbane, Australia

(Petley et al., 2008) and can be accommodated along the entire rupture, generating low localised strains.

Temporal resolution of monitoring patterns of non-linear and/or rapid deformation may fall 2.below the frequency of monitoring.

Small, apparently random displacements unsuitable for the detection of larger scale 3.deformation. Rose and Hungr (2007) noted that large rock slides rarely move as coherent mass, rather small localised movements such as buckling and toppling may ensue. They suggested that, though these are related to strain development, characterisation of localised superimposed failures may not reflect wider-scale deformation.

Cyclical changes including wetting/drying of the slope and temperature/pressure variations 4.which act across large open pits (Crosta and Agliardi, 2003). These have the potential to invoke decimetre scale movements.

Stress triggering mechanisms may superimpose individual cycles on overall movement trends or 5.instigate a transition from regressive (decelerating) to progressive (accelerating) movement (see Zavodni, 2000). Examples of such mechanisms include excavation of failure surfaces and removal of buttressing at the slope toe.

Instrument errors including data projection angles normal to direction of movement. Rosser 6.et al. (2008) showed that inferred deformation of a slope inclined at 23 increased by nearly an order of magnitude for every 5 deviation from viewing angles normal to the face.

A number of challenges thus arise. First, high resolution and precise monitoring of the entire slope is required to examine deformation operating across a range of spatial and temporal scales, with some related to trends in overall slope deformation and others to localised stress-triggering mechanisms. Second, with regard to terrestrial laser scanning, deformation of the slope is based upon range measurements relative to the scanner. The overall vector of movement in the majority of scenarios, however, is normal to the direction of scanning, thus deformation becomes difficult to resolve. Akin to this is the quantification of river discharge solely by monitoring changes to the water surface height from an aerial view (see Rosser et al., 2008). An alternative observable change to the rock slope during pre-failure movement is surface orientation, or incline, relative to the scanner. As such, this study examines the sensitivity of full waveform terrestrial laser scanning (FW-TLS) to changes in target geometry, encompassing changes to both target range and incline.

In conventional TLS systems, range is estimated using an algorithm which automatically detects an undisclosed feature of the reflected laser beam (known as the waveform); this may be a threshold of the reflected energy or the maximum amplitude. Critically, though conventional TLS systems can characterise slopes at an unrivalled spatial resolution, they do not record the structure of the reflected waveform. Full waveform TLS captures and digitises the full structure (energy-time distribution) of the waveform offering more measures of change than range alone. In the system used in this study, the waveform is recorded at 2.01005 10-9 s intervals, providing 1590 amplitude measurements per beam. This allows the reflected laser energy to be considered in a calibrated sense as the total reflected energy can be summed and compared for each beam.

Despite divergences of less than 1 mrad, pulses may encounter multiple objects during flight; accordingly, the full waveform of the backscattered pulse has enabled filtering of reflective objects such as vegetation (Fowler et al., 2011; Jaboyedoff et al., 2012). In both conventional and full waveform LiDAR systems, the energy of the received beam structure depends on the scanner mechanism, the spatial energy distribution of the emitted beam, and the geometric and reflectance properties of the surface (Stilla and Jutzi, 2009).

Slope performance


The scanners angular resolution refers to its ability to resolve two objects in adjacent lines-of-sight (LOS) as determined by the sampling interval (user-defined point spacing) and beam width. If beam width exceeds the sampling interval, fine details become blurred. Beam width is indicative of the spatial energy distribution of the laser beam as it strikes the target and is referred to as spot dimension ( ). The spot dimension is partially determined by the instrument-object distance (Petrie and Toth, 2008) and aperture size:

(1)

Where:

= spot diameter (m).

= initial spot diameter (beam aperture) (m).

= instrument-object range (m).

= beam divergence (rad).

Beam divergence is typically specified by the manufacturer; for example, the Riegl VZ-1000 utilised in this study has an aperture width of 8 mm and a divergence of 0.3 mrad, corresponding to a beam-widening of 30 mm per 100 m of flight assuming a circular beam.

The energy distribution of the emitted beam is Gaussian (Lichti et al., 2002); accordingly, the beam energy is greatest at the central time instant of the pulse. This enables a comparison of the emitted and received waveforms where difference is influenced by the geometric and reflectance properties of the target.

The energy of the reflected pulse is partially dependent on the physical attributes of the target surface. These include electric permittivity, magnetic permeability and conductivity, the impacts of which are wavelength dependent (Lichti et al., 2002). Rock slopes with high levels of water seepage, for example, may increase the proportion of spurious measurements due to elevated levels of scatter. This study assumes that the surface used scatters the received laser pulse in a Lambertian manner, uniformly in all directions.

The angle of incidence is the angle between the laser beam and the vector normal to the surface and may change with deformation of the rock slope. It can be related to spot dimension by:

(2)

Where:

= angle of incidence (rad).

As in Equation 1, range refers to the distance between the instrument and the target. Greater range and incidence angles have been shown to decrease the signal-to-noise ratio (SNR) of resulting point clouds as less intense signals are less likely to be detected (Soudarissanane et al., 2011) . A signal is weak when the received power is below the noise level threshold of the detection unit.



Figure 1 illustrates that the incidence angle constitutes the cosine of the vector normal to the scanner and the beam. If the laser beam strikes the surface with a non-zero incidence angle, the resulting footprint is elongated; thus spreading the energy distribution over a greater target surface area. Using derivatives of the radar equation, Soudarissanane et al. (2011) note that the SNR of a laser return deteriorates with the cosine of the incidence angle. Moreover, SNR deterioration is inversely proportional to the square of range.

The use of FW-TLS in this study enables measurement of detailed changes to the structure of the waveform in order to provide an alternative measurement of the influence of target geometry relative to conventional TLS systems. Though a number of studies have sought to characterise the reflected structure of airborne LiDAR systems (e.g. Stilla and Jutzi, 2009), none have yet been applied to constrain change to the waveform from terrestrial platforms. This is particularly important on near-vertical deforming rock slopes and benches where the instrument-object range may remain constant despite tilting/inclination of the surface.

A 1 m2 board, painted white to enhance the reflection of the signal, was rotated about its vertical axis at instrument-object ranges 10, 20, 30, 200, 400 and 500 m. At each distance, rotation was undertaken in 5 increments between 0 and 60 normal to a Riegl VZ-1000 scanner equipped with full waveform capacity.

Slope performance


The boards initial orientation was established as normal to the direction of scanning using electronic distance measurements of either side of the board, with both sides yielding equal ranges from the scanner. Although the board is not assumed to act as a perfectly Lambertian reflector, the relative degree of anisotropic reflectance is anticiapted as small due to the smoothness of the white painting.

The waveform data was extracted and then prepared for analysis using Stata statistical software, though similar analysis is also effective within MATLAB. Points were spaced at 3 cm on the board and the central portion cropped to remove reflected signals at the boundary between the board and its aluminium frame (Figure 2). Using Equation 1 for the VZ-1000 scanner, the spot dimension at 400 and 500 m is 0.12 and 0.15 m respectively. As such, the central two-thirds of the board were cropped to create a distance of 0.17 m from the frame.

Change to the waveform at a set distance of 200 m was examined and the mean reflected waveform from each board inclination created (Figure 3). From visual inspection, it is clear that only a very slight increase in pulse width, typically measured at half of the maximum amplitude (Stilla and Jutzi, 2009), is exhibited with target incline. Furthermore, the waveform retains a very similar shape despite change to the angle of incidence. Changes to the maximum amplitude, however, appear far more sensitive to incidence angle alteration. As larger incidence angles result in a more elongated footprint, the reflected photons are returned over a greater time period, thereby reducing the waveform amplitude and widening the reflected pulse width (Stilla and Jutzi, 2009). This can be shown to adhere to the radar equation:

(3)

Where:

= received signal power.

= constant relating aperture size, target reflectance and atmospheric conditions.

= emitted signal power.

= incidence angle.



The peak amplitude of the waveform has previously been used to map spectral properties of minerology within geological outcrops. Kaasalainen et al. (2005), for instance, found that surface brightness greatly increases the the backscattered peak amplitude. Although this study negated the effect of incidence angle on this peak value, the homogeneouos surface brightness exhibited by the board in the current study shows that the received waveform is in fact sensitive to changes in incidence angle. This bears significant importance for mines in which conventional laser scanners are installed and the vector of movement in deforming rock slopes is normal to the direction of scanning. Maximum amplitude may be used to identify changes on a pixel-by-pixel basis in scenes where incidence angle may better indicate deformation than range measurement.

In Figure 4, the change in maximum amplitude with incline for 200 m is plotted. The break in gradient appears to indicate that for inclines greater than 45, a greater proportion of the laser energy is reflected away from the scanner, thus weakening the returned signal. Similarly, Lichti (2007) modelled residual range measurements relative to a plane and found a similar deterioration (increase in residuals and hence noise-to-signal ratio) at 65, proposing this as a threshold for the a priori removal of outlier points. Critical to the application for rock slope monitoring, the percentage decrease in peak amplitude between 45 and 60 (35.6%) illustrates a greater sensitivity than for incidence angles below 45 (19.4%). In practice, scanning rock masses at higher incidence angles is problematic and yields point clouds with greater SNRs; however, the enhanced sensitivity of the waveform appears to suggest that deformation within this range of inclines may be more effectively characterised. Somewhat surprisingly, the standard deviation of the peak amplitude value for all pulses extracted from the board decreases with incidence angle, further reinforcing the potential successful application of peak amplitude monitoring at high target inclines.

Slope performance


Figure 5 illustrates the simultaneous effect of influence angle and distance on the reflected waveform structure. As illustrated in the previous section, the maximum amplitude reduces for larger inclines at all distances. Although the variation in absolute values of amplitude appears to be minor in the waveform structure at 400 and 500 m, similarly shaped waveforms with peak amplitudes do exist at these distances with statistically significant variation in amplitude. It is clear, therefore, that although the reflected waveform structure remains constant, the peak amplitude reduces in proportion to both the instrument-object range and incidence angle. This can be explained using Equations 1 and 2 respectively, which demonstrate that greater instrument-object ranges and incidence angles will result in a larger footprint, thereby spreading the pulse energy over a larger radial area and weakening the maximum returned amplitude.

In addition to the peak amplitude, the amplitude at any given time increment diminishes with incline and distance. As such, the area beneath the waveform, derived from trapezoidal integration between time increments 1 and 10, is plotted in Figure 6 as an alternative parameter sensitive to change. At all distances, greater incidence angles result in smaller areas beneath the curve and hence the total energies of the reflected waveform. With varying distance, the total reflected energy decreases hyperbolically. Although absolute changes to waveform area with incidence angle are lower at large distances, the relative change remains constant. This suggests that, because the absolute energy change of the reflected pulse is lower, the detectability of changes to target geometry will be dependent on the receivers sensitivity at greater distances.



(a)

(b)

Slope performance


Simple attributes, such as the amplitude of the mean reflected laser shot for each cropped area, are available in Riegls RiSCAN PRO software (Riegl, 2013). In Figure 7, variation in the mean maximum amplitude illustrates a strong correspondence between the full waveform derived measurement and the conventional calibrated amplitude value. For the same inclines, however, the overall rate of change in peak amplitude with distance is greater based on analysis of the full waveform. In particular, the curves for each incline remain broadly parallel and exhibit a greater spacing in Figure 7(b). It is thus apparent that despite using the same scanner and the same data, analysis of changes to the waveform structure better discerns variation in surface geometry. Details regarding (a) the time increment at which the amplitude is measured and whether this is a mean/maximum value, and (b) the scaling from actual waveform amplitudes in RiSCAN PRO remain an industrial secret and may be partly responsible for this discrepancy.

(a)

(b)



Deviation, which describes the change in shape of the received waveform relative to that emitted, is another parameter provided in RiSCAN PRO and is plotted simultaneously against distance and incline (Figure 8). Larger values indicate increasing disparity between the emitted and reflected structures, whilst those closer to 0 indicate greater similarity. Although the manufacturer identifies target incline as the predominant influence over this value, an increase in deviation of the reflected waveform structure is only evident at ranges of 10, 200 and 500 m. This figure clearly shows that monitoring of deviation values without the ability to resolve the full waveform is an unreliable indicator of surface geometry change.

The effect of incidence angle has previously been observed (Kaasalainen et al., 2005; Kremen et al., 2006) and modelled (Lichti, 2007; Soudarissanane et al., 2009) in relation to the signal-to-noise ratio of the resulting point cloud. In this study, change to the maximum amplitude was found to be a sensitive indicator of target incline at a set distance. Although rotation for any given point on a rock is less likely to operate around the vertical axis as the horizontal, the sensitivity of the waveform to alterations in target incline nevertheless illustrates its potential for integration into rock slope monitoring practice. For example, the relationship between maximum amplitude and incline in Figure 4 may be used to infer pixel-by-pixel rotation of a rock slope relative to the scanner based on changes to the waveform. This is especially so for pixels inclined above 45 as, after this point, shifts in surface geometry are more clearly manifested in the waveform. Rosser et al. (2008) observed that when a deforming slope was rotated away from a viewing angle normal to the direction of movement, every 5 rotation about its long slope axis increased the inferred deformation by nearly an order of magnitude based on range measurement. In many open pit monitoring scenarios, however, viewing angles are normal to the overall vector of movement and, as such, analysis of change to the waveforms maximum amplitude may help to better characterise deformation.

In addition to the observations made at 200 m, the waveform is sensitive to changes in incidence angle at 10, 20, 30, 400 and 500 m. In Figure 5, it is also evident that maximum amplitude and area (Figure 6) reduce with increased target range, raising three points of note. First, changes in target range measured using the time-of-flight of the laser shot can be supplemented by changes to the amplitude of the waveform. This provides the potential to validate conventionally derived strain estimates used in deformation monitoring

Slope performance


and failure-prediction. Second, the waveform should be normalised to account for the effect of range prior to analysis of surface incline. Third, changes to the maximum amplitude and deviation recorded by a conventional TLS system (Section 4.3) appear less sensitive to shifts in surface geometry, again suggesting that the full waveform capacity is a useful supplement to conventional calibrated data.

At present, FW-TLS can be considered a new technology and many questions remain to be answered. The response of the waveform to changes in real rock slopes remains to be examined and is likely to be site-specific based on local variables such as surface roughness. In addition, theory dictates that the skewness of the reflected energy-time distribution will change according to Figure 1; while surfaces normal to the scanner produce circular cross-sectional footprints in which all parts of the beam are reflected uniformly in time, some parts of the beam reflect sooner than others when it becomes elongated due to greater surface incline. The absence of any change to the waveform shape in this study may be related to uncertainty in the start time of sampling recorded by the receiver.

Measurement of the duration of the reflected waveform also remains ambiguous; in Figure 5 a reduction in energy is recorded immediately after the beginning of sampling and the beams emitted closer to the target do not return to the same amplitude at the end of sampling. The former complexity may be related to background illumination if it is higher than the energy of the received waveform, whilst the latter may indicate saturation of the receiver. Although the received waveform is clearly sensitive to changes in target geometry, further controlled experiments are required to determine the influence of other variables, such as surface shape and texture, which will also affect the resulting waveform on heterogeneous rock slopes.

At present, terrestrial laser scanners in open pit mines are capable of collecting highly precise range estimates at high spatial resolutions. The ability to resolve deformation normal to the scanning direction, however, is impossible with the sole use of range estimates. In this paper, we have demonstrated for the first time that the reflected waveform is sensitive to changes in target geometry. With further application in open pit monitoring, this technology has the potential to provide valuable information on deforming rock slopes which may not be discernible using techniques of lower spatial resolution or with an inability to capture such surface attributes.

The authors wish to thank Siobhan Whadcoat and Stuart Wallace of Durham University for their assistance during the experiments. This research forms part of an ongoing Ph.D. project funded by the Engineering and Physical Sciences Research Council (EPSRC).

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51_williams.pdf

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