Acquisition of Complete Acoustic Emission Amplitude Records during RockFracture Experiments
S.D. Goodfellow1, J.W. Flynn2, J.M. Reyes-Montes2, M.H.B. Nasseri1 and R.P. Young1
1
Department of Civil Engineering, University of Toronto, Toronto, Canada
2
Applied Seismology Consultants, Shrewsbury, United Kingdom
Abstract
This paper presents the results from a triaxial deformation experiment where acoustic emission (AE) waveforms were continuously recorded at two different gain levels. The purpose ofthis work is to quantify the extent of missing amplitude data, which is typically lost at criticalpoints during rock fracture experiments due to waveform clipping. A cylindrical sample ofWesterly granite was axially loaded until failure at a constant displacement rate and 25 MPa ofconfining pressure. AE was monitored by 18 piezoelectric sensors mounted on the cylinder ends
and around its circumference. AE data was continuously acquired and digitized at 10 MHz and12-bits for the duration of the experiment where four channels were amplified 6 dB and the rest
40 dB. Two large stress-drops occurred in the post peak-stress regime resulting in large amplitude bursts of AE. Channels amplified 40 dB showed complete amplitude saturation, making
event analysis difficult whereas those channels amplified 6 dB remained unclipped. We conclude
that the amount of missing amplitude data is significant for experiments of this nature and should
be considered when designing AE acquisition setups.
1. Introduction
Acoustic emission (AE) monitoring is a technique used to monitor damage accumulation inengineering structures and brittle materials under load. When local stress conditions, at the micro
scale, reach a critical level, fractures will form and release energy, some of which is in the formof elastic waves. To study the failure process of different materials under controlled conditions,laboratory experiments are conducted under different loading conditions. A common experimentin experimental rock mechanics is the triaxial deformation test. In this experiment, a cylindricalrock sample is brought to a hydrostatic stress using steel loading platen on the cylinder ends andsilicone oil via a rubber sleeve. From this point, the rock is loaded to failure by compressing therock along the cylinder axis at a constant displacement rate. In the post peak-stress regime,
stored elastic energy in the rock and steel platens forces fractures to propagate dynamically leading to catastrophic failure.
At the time of catastrophic failure, AE is difficult to analyze because the acquisition systemsaturates. Schubnel et al. [1] studied fluid-induced rupture of Fontainebleau sandstone during
triaxial deformation experiments. During dynamic fault propagation, they assumed the period offull waveform saturation (5 ms) was related to the rupture time, which provided an upper boundfor the rupture velocity of 4 m/s. Thompson et al. [2] studied the failure of intact rock using acontinuous AE acquisition system. Channels used to source locate events were amplified 40 dB.
As the sample approached failure, the waveform amplitude significantly increased, resulting in a
95 ms period prior to failure where no AE locations could be obtained. Additionally, amplitude
saturation can cause issues when calculating frequency-magnitude statistics.
Lei et al. [3-7] solved this problem by using two acquisition systems during their experiments. A full waveform recorder, capable of switching between triggered and continuous modes,
was used to record waveforms, which were used for source location and mechanism analysis.
J. Acoustic Emission, 32 (2014)1© 2014 Acoustic Emission GroupChannels hooked up to the full waveform recorder were amplified by 40 dB. Maximum waveform amplitudes from two AE transducers were recorded on a separate acquisition unit with 99
dB dynamic range.
Recently, an effort has been made to bridge the gap between AE produced during mm scalelaboratory rock fracture experiments and larger natural earthquakes by using quantitative seismology theory [8, 9]. For this type of analysis, unclipped amplitude records of the complete AE
catalogue are essential for studying events over a wide range of magnitudes.
In this paper we study AE produced during dynamic failure of rock specimens, which was
amplified at two different levels. By studying unclipped waveforms at the time of catastrophic
failure, we hope to gain insight into the nature of waveforms acoustically radiated during dynamic rupture propagation.
2. Experimental Procedure And Data Acquisition
2.1 Sample Preparation
Westerly granite was chosen because it has been extensively studied and is well suited to AE
studies [2] because of its homogeneous and isotropic structure. The mean grain size is 0.75 mm
and modal analyses give the following mineralogical composition in volumetric percent:quartz 27 %, microcline 36 %, plagioclase 30 %, phyllosilicates 6 % and others 1 % [10].
A cylindrical sample 125 mm in length and 50 mm in diameter was prepared conforming to
the 2.5 length/diameter aspect ratio recommended by the International Society of Rock Mechanics (ISRM, 1977 [11]). The sample was prepared using a hollow coring drill, and its end faces
were ground and polished to within 0.01 mm parallelism using a diamond CNC grinding wheel.
After the sample was machined to the specifications mentioned above, it was thermally treated ina high-temperature oven to induce micro-fractures along grain boundaries. The sample was heated to a temperature of 850oC, at a rate of 2oC/min. In this experiment, the reason for thermallydamaging the rock was to promote a more controlled failure to maximize the number of recorded
AE waveforms. Lei et al. [3] reported that pre-existing cracks are the most dominant factor of allheterogeneities that govern fault nucleation in laboratory rock fracture experiments.
2.2 Triaxial Deformation Cell
A triaxial deformation cell (ErgoTech Ltd.) equipped to handle a cylindrical rock specimen
was used to conduct the reported experiment (Fig. 1). The axial load was applied by an external
hydraulic actuator (MTS) operated under displacement control and the confining pressure wasdelivered via silicone oil and a rubber jacket. Two pairs of orthogonal cantilevers were used tomeasure diametral strain, while a pair of LVDTs measured axial strain. The cell is equipped with
18 piezoelectric sensors for monitoring AE activity. The AE sensors (designed by Laszlo Lombos, formerly of ErgoTech Ltd.) are sensitive to surface normal velocity and were calibrated between 100 kHz and 1.4 MHz using a laser interferometer [12]. Three AE sensors are located ineach loading platen (spring loaded) and the other 12 are embedded in the rubber confining jacket(Fig. 1). AE sensors have a contact area diameter of 3 mm. Three additional pairs of ultrasonicsensors for contemporaneous measurements of P, S1 and S2 waves are located along three orthogonal axes.

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Fig. 1: Triaxial geophysical imaging cell.

Fig. 2: Acoustic Emission acquisition setup.
2.3 Acoustic Emission Data Acquisition
AE signals were amplified using Applied Seismology Consultants (ASC) preamplifiers,
which include a 100 kHz – 1 MHz band-pass filter module in each unit. Data from 18 AE
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sensors was acquired continuously at a sampling rate of 10 MHz and 12-bit resolution using the
ASC Richter system. Typically with 40 dB amplification, less than a second before brittle failure
the acquisition system saturates resulting in clipped waveforms. In an attempt to acquire full, unclipped waveforms during failure of the specimen, some channels had their amplification reduced. During the reported experiment, raw AE signals on 14 channels (Fig. 2) were amplified
by 40 dB and the remaining 4 channels were amplified by 6 dB. After the experiment, the continuous AE waveforms were harvested to extract discrete events. A trigger was defined at fourchannels recording a voltage of more than 80 mV (i.e. threshold voltage) within a window lengthof 512 sample points (51.2 µs). If this criterion was met, a waveform length of 1,024 sample
points for each channel was written to file.

Fig. 3: a) Differential stress, b) inelastic strain and AE count, c) P-wave velocity and d) P-wave
velocity anisotropy.
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3

Results

3.1 Basic Analysis
The sample was loaded hydrostatically to 25 MPa and then differentially until failure at aconstant displacement rate of 0.2 µm/s. In Fig. 3a the differential stress is plotted against axial
strain up until just before final failure. The initial portion of the stress-strain curve shows plastic
deformation caused by the closing of grain boundary micro-cracks, which were induced during
heating. Once the micro-cracks were closed, the rock responded linearly to continued axial
shortening. Figure 3b displays the inelastic strain and cumulative AE counts. The stress-strain
curve diverged from linearity at a differential stress and axial strain of approximately 256 MPa
and 1.1% respectively. AE signals began to appear as the inelastic strain started to increase. Cumulative AE reached a maximum value of 10,000 counts before final failure. In Fig. 3c the axial(VpV) and lateral (VpH) P-wave velocities are plotted against axial strain. The axial P-wave velocity gradually increased during the pre-failure measurement period whereas the lateral velocity
started to decrease at a point that roughly coincided with initiation of inelastic strain and sample
dilation. The difference in axial and lateral velocity trends is caused by the creation of microfractures aligned parallel to σ1. Figure 3d presents the P-wave velocity anisotropy, which is thepercentage difference between the axial and lateral P-wave velocities. P-wave velocity anisotropy was roughly 0 % at a hydrostatic stress of 25 MPa and increased steadily to almost 50 % after
final failure.
3.2 Continuous Acoustic Emission Waveforms
In Figs. 4a and 4b, differential stress and AE waveform amplitude from channel 16, amplified at 40 dB, are plotted against loading time for the last 3,600 s and 200 s of the experiment,
respectively. After peak stress, there were two stress drops (SD1 and SD2), which were separated by roughly 18 s. The first stress drop was 30 MPa and the second was 305 MPa. Leading up
to SD1, the waveform amplitude ramped up and the ±5 V dynamic range of the acquisition system was saturated almost continuously for 18 s between stress drops. Figure 4c displays the
waveform amplitude for Channel 17, which was amplified at 6 dB, for a 45-s interval spanning
both stress drops. With the lower amplification level, the acquisition system did not saturate during dynamic failure.
3.3 Acoustic Emission Hypocenter Locations
P-wave arrivals were automatically picked and event hypocenter locations were calculatedusing the downhill Simplex method. We were able to locate 4,357 events from roughly 20,000
triggers harvested from the continuous waveforms. The majority of AE events occurred between
the last two velocity surveys, during which time the axial P-wave velocity decreases from 5,174
m/s to 4,506 m/s and the anisotropy increases from 38.76% to 57.5%. To improve the source location accuracy, a time varying velocity model was employed.
AE source locations were divided into four time bins and are plotted in Figs. 4d - 4k. Two
faults were induced during sample failure and are highlighted in red and blue where the red fault
was induced during SD1 and the blue fault during SD2. Nucleation began in the upper left side at
the sample’s edge and lasted for roughly 5 seconds before SD1. Coinciding with SD1, a cloud of
AE locations propagates rapidly towards the bottom of the sample. There is a small gap in AE
locations (green line), which is a result of cascading micro-fracture events that occurred in suchclose succession and such large amplitudes that individual events could not be easily identified.
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Fig. 4: a, b) Channel 16 waveform (40 dB) and differential stress, c) Channel 17 waveform (6dB), d-g) AE source locations scaled to the maximum amplitude recorded on the 40-dB channels,h-k) AE source locations scaled to the maximum amplitude recorded on the 6-dB channels.
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In Figs. 4d – 4g, event locations are represented by black circles whose radii were scaled tothe maximum recorded amplitude on the 40 dB channels. This means that the event radii werescaled by voltages ranging from ±0.4 mV (noise floor) to ±0.05 V (saturation level of the acquisition system at 40-dB amplification). In Figs. 4h – 4k, radii were scaled to the maximum recorded amplitude from either the 40-dB or 6-dB channels, meaning radii were scaled by voltages
ranging from ±0.4 mV (noise floor of the 40-dB channels) to ±2.5 V (saturation level of the acquisition system at 6-dB amplification).
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Discussion

In Figs. 4h – 4k, we scaled event radii by the combined maximum recorded amplitude fromthe 40-dB and 6-dB channels. Visualization of the complete amplitude record presents new perspectives for studying the unstable failure process of geo-materials. Figure 4h shows the rapid
propagation of the first fault, which produced a number of events with significantly larger amplitudes than the majority. Additionally, large amplitude events appear to be located on portions of
the fault where there were no dense clouds of smaller amplitude events. This suggests that thelarge amplitude events are associated with the rapid propagation of fractures or coalescence ofpreviously induced micro-fractures.

Fig. 5: Continuous AE waveforms from channels 4, 7, 14, and 17, amplified 6 dB during SD1
including the sensor locations.
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Figure 5 presents differential stress and axial strain plotted against loading time for SD1. At
this critical point, channels amplified 40 dB are completely saturated making identification of
discrete events difficult. Included in Fig. 5 is a diagram of the rock sample with the 6-dB sensor
locations and geometry of the induced fault structure. In Figure 5, waveforms from channels 4, 7,
14, and 17, amplified 6 dB, show no sign of saturation during SD1. During the height of fracturing, the waveform appears as a quick succession of discrete large amplitude waveforms superimposed on a background of micro-fracturing noise.

Fig. 6: Continuous AE waveforms from channels amplified by 40 dB and 6 dB (16 and 17) during SD1 including the sensor locations.
Waveforms from channel 16 (40 dB) and channel 17 (6 dB) are presented in Fig. 6. AE sensors connected to channels 16 and 17 are located next to each other on the bottom loading platen
and therefore the amplitude of incident wave fronts should be approximately the same. Waveform 1 was recorded 0.2 seconds before SD1 and shows good signal to noise ratio (SNR) on
channel 17 (40 dB) whereas no waveform is recorded on channel 16 (6 dB). During SD1, waveforms 2 and 3 show clipped signals on channel 16 and clear discrete waveforms on channel 17.
The results presented above show two distinct amplitude regimes. In the first regime, AE activity is produced by grain scale micro-fracturing. This regime continues through nucleation,
coalescence and quasi-static fault propagation right up until the point of instability. In the second8regime, the fault propagated dynamically producing large amplitude waveforms in rapid succession. Figure 7 shows channel 16 (40 dB) waveform superimposed on channel 17 waveform.
Waveform 1 occurred during the first regime where channel 16 recorded a multitude of small
amplitude events with good SNR. Channel 17 shows a coarse resolution with low SNR and mostof the small amplitude waveforms buried in the noise. Waveform 2 occurred during the second
regime where channel 16 is almost continuously saturated but channel 17 displayed full, unclipped waveforms. These results highlight the need for multi-scale acquisition during rock fracture experiments.

Fig. 7: Continuous AE waveforms from channels 16 and 17 superimposed on each other for 35
seconds spanning SD 1 and SD2.
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5Conclusion

In this paper we presented the results from a triaxial deformation experiment on a cylindricalsample of high-temperature-treated Westerly granite. AE data from 18 sensors was continuously
digitized at 10 MHz and 12-bits resolution for the duration of the experiment. 14 channels were
amplified 40 dB and the remaining 4 were amplified 6 dB. Approximately 20,000 events were
harvested from the continuous waveforms and a time-varying velocity model was used to determine the source locations of 4,357 events. Analysis of AE locations showed the growth of an initial fracture that arrested on the bottom loading platen, and was followed by the growth of a second fracture resulting in the final 305 MPa stress drop.
Analysis of continuous waveforms from 40-dB and 6-dB channels demonstrated a wide
range of waveform amplitudes. Amplitudes ranged from ±0.4 mV (noise floor of the 40-dB
channels) to ±2.5 V (saturation level of the acquisition system at 6-dB amplification). We identified two regimes of fracturing: (1) micro-fracturing produced during nucleation and quasi-static
fault propagation, which generated small amplitude waveforms and (2) dynamic fault propagation producing large amplitude waveforms. Typically in rock fracture experiments, the data acquisition setup is optimized to capture waveforms from the first regime, which is used to track
the spatial evolution of damage. We showed that by reducing amplification during the second
regime, discrete unclipped waveforms can be recorded and analyzed bringing new information
about the failure process of rock under elevated stress.
Acknowledgements
We would like to thank Laszlo Lombos and Applied Seismology Consultants for their technical support. The funding for the creation and operation of the Rock Fracture Dynamics Facility
at the University of Toronto was provided by the Canada Foundation for Innovation and theProvince of Ontario (Grant no. 0000302419) and the Natural Sciences and Engineering ResearchCouncil of Canada (Grant no. 0000300001), respectively.
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