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2 .7 .2 .1 .

Acoustic Emissions.

Acoustic activity during deformation was first noticed by Obert & Duval (1942) when they loaded rocks in compression. They attributed the activity (correctly) to micro-cracking. The obvious similarity between AE and field

micro-seismicity has led to laboratories throughout the w orld making significant advances in the pursuit of AE research. Being a natural by-product

of brittle crack formation and grow th, it presents itself as a useful tool for investigating the mechanics of brittle failure in rocks.

The study of brittle processes has developed in a number of directions and these are briefly covered below. Section 4.6. discusses the terminology and AE phenomena that are used to monitor damage accumulation.

Fracture Event Location. Employment of four or more transducers and analysis of elastic wave arrival times allows the 3-D location of cracking events to be mapped. Fifteen or more transducers are commonly used for greater accuracy, allowing the source event to be located to w ithin 2mm. The method is analogous to seismic networks for locating earthquakes. Using an AE feedback system to control axial stress and hence AE event rate (to slow down the localisation of events and strain), Lockner et al. (1992) successfully mapped fault nucléation and development, (see fig 2 .7 .2 .i.e .).

The results show pre-nucleation activity, fault nucléation and fault propagation clearly. During fault grow th the fault front was seen to propagate erratically, sometimes accelerating and sometimes decelerating at different segments of the fault.

ll •

. . . K

■ :

Fig. 2.7.2.1 . a . Time sequence of a e events showing the complete fault formation process,

the two rows are views orthogonal to each other, (After Lockner 1993).

Cumulative AE, Event rate and seismic b-value analysis. Cumulative number

of AE events and the event magnitude are the simplest parameters to record (requiring only one piezo electric transducer), and hence have been used most extensively in rock deformation studies. Scholz, (1968a, b, & c), by increasing the frequency response of a barium titanate compressionai mode

transducer and employing faster monitoring equipment, improved the

sensitivity of the ae recording system by tw o or three times compared w ith previous workers. This paved the way for more detailed micro-seismic activity studies. His study confirmed earlier work by Brace et al. (1966), which pinpointed the onset of dilatancy in rocks under increasing stress, and also revealed ae to be closely related to stress/strain behaviour; dilatancy

began at about one third to tw o thirds of peak stress, initially randomly distributed, follow ed by rapidly increasing m icro-fracturing prior to the formation of the fault. Cumulative a e gives an indication of the total damage accumulated in the specimen through dilatant cracking, and can be used to correlate brittle crack grow th w ith dilatant volume increase.

A statistical model devised by Scholz successfully predicted the pattern of micro-cracking in laboratory specimens. The G riffith criterion for failure was deemed inapplicable to crystalline rocks on the basis o f this w ork due to rocks' heterogenous nature and hence its m icroscopically heterogeneous stress field. These internal stress fluctuations cause microcrack grow th at much lower macroscopic stresses than in homogeneous materials, and arrest cracks shortly after they have initiated. Hence, unstable runaway microcrack propagation and material failure does not occur until higher stresses. In 1968 Scholz also related the commencement of fault form ation w ithin a stressed and fractured medium to it reaching a critical crack dertsity.

From statistical analysis of the numbers of a e events of different magnitude a microseismic b-value can be determined, (section 4 .5 .). It has been used to correlate earthquake activity w ith laboratory specimen behaviour (Mogi 1962b, Main et al. 1989). M ogi's hypothesis regarding the striking similarity between b-values in earthquake ruptures and laboratory specimens was further upheld by Scholz (1968b). Scholz, however, also found b-values to be closely dependent upon stress levels, and Fonseka, Murrell & Barnes (1985), showed the b-value to decrease to a minimum at failure.

Meredith & Atkinson (1983) found a good correlation between AE data and

crack tip stress intensity factor in tensile crack grow th experiments on Whin Sill dolerite. They conclude that stress intensity factor and crack grow th rate have and an identical functional relation to AE (see fig. 2 .7 .2 .i.b .), and hence AE event rate can be used as a remote m onitor of crack velocity (in tensile mode I fracture mechanics tests). It was further found th a t because the size distribution of a e events obeys a power law function, Meredith, Main & Jones (1990), showed th a t this implies that a power law distribution of crack sizes

D varies with stress. In compressive tests, however, AE events prior to ultimate sample failure come from cracks which eventually stabilise, and subsequent AE is due to the growth of new cracks.

Physically, a high b-value represents emissions dominated by small events and a low b-value represents emissions dominated by large events. Test conducted in water showed consistency higher b-values in the lower K range

{ 0 A > K / K ^ > 0 . 8 ) when compared w ith dry experiments. SEM studies indicate a predominance of transgranular cracking at high crack velocities and in low humidity (high K), and intergranular cracking at low velocities and high humidity (low K). This is attributed to chemically induced cracking at grain boundaries, since grain boundaries provide the main conduits for fluid movement. Good correlation has also been found between inelastic strain, cumulative AE, and rock damage. Hatton (1992), found sudden jumps in strain during creep tests on granodiorite correlated w ell w ith bursts of ae.

Cox & Meredith (1993) related AE data to microcrack form ing events. The data is used to represent a damage state variable. Damage accumulation models by Bruner and Walsh are then used to successfully predict physical property changes in the rock during deformation. W hilst the work leaves one or tw o details outstanding (e.g. an independent calibration of the scaling relation between the acoustic emission parameters and the microcrack geometry), the work demonstrates how quantitative analysis of AE data may be used to infer the

K i ( M P a . m S )