Seismic monitoring systems installed in mines record seismic signals from various dynamic processesfracturing in rock mass, production and development blasts, impacts and vibration of machinery, etc. It is important to classify the recorded seismic events before doing most kinds of analysis of seismic data. For instance, the assessment of seismic hazard or analysis of rock mass stability requires only events associated with fracturing in rock mass. A discrimination technique developed in global seismology was adopted for mining environments in order to identify blasts. The technique utilises a set of seismic event characteristics (time of occurrence relative to blasting time, radiation pattern, distribution of seismic energy between low-and high-frequency bands, correlation of the seismic signals with the preceding and succeeding waveforms) and quantifies a probability that a particular event belongs to a population of blasts. The application of the technique to several mines is discussed.
In underground mines, episodes of sudden inelastic deformation in the rockmass are often induced by mining and are therefore localized near the excavations. The seismic radiation associated with such deformation can be described by a point seismic source using a volume integral of the stress-free strain (or incremental plastic strain)—expression (1) in the Theory section. This conventional description is valid if seismic waveforms are modeled or inverted using the elastodynamic Green’s function, which takes the presence of nearby excavation(s) into account. If the adopted Green’s function does not model these excavations, then the conventional expressions for a point source need to be adjusted by adding a term that depends on the displacement at the surface of the excavations (equations 2). Alternatively, a Kirchhoff-type representation can be used, in which the parameters of the point source are expressed using increments of displacement and traction over the surface covering both the volume of inelastic deformation and nearby excavation(s) (equations 3). Numerical simulations demonstrate that the suggested expressions provide a very different result to the conventional expressions for the case of inelastic deformation in a volume adjacent to an excavation. The utilization of the suggested expressions results in a change in the type of mechanisms from explosive to implosive and significantly affects other characteristics of the modeled sources (moment magnitude and orientation of principal axes). Typically, seismic waveforms recorded by monitoring systems in underground mines are processed using an elastodynamic Green’s function that does not take medium-sized excavations (e.g., tunnels and stopes) into account. Therefore, the results of such processing need to be interpreted using the suggested expressions: adjusted conventional (equations 2) or Kirchhoff-type (equations 3).
An approach to model seismicity induced by mining was proposed by MDG Salamon and developed further by AM Linkov. We suggest a variation of the Salamon-Linkov method that does not require a priori assumptions about sizes, orientations and locations of flaws, reducing the amount of input data. The algorithm places a 3D grid over the model domain and searches the entire grid space for possible planes of failure by identifying lobes of positive Coulomb Failure Stress (CFS). The largest CFS lobes are then utilised as flaws. Another change from the original formulation is that the flaws are discretised into smaller sub flaws. This accounts for the complexity of the shapes of the flaws and provides more accurate modelling of their effect on the stress field. Some elements of the original framework of Salamon and Linkov, e.g. tensile failure and creep effects are not included into our implementation. This is done intentionally to keep the method simple at the current stage. The implemented method requires limited input data: models of mining steps, information about virgin stress, rock mass elastic properties and strength. The strength may be anisotropic and inhomogeneous, where specific Mohr-Coulomb failure criteria can be assigned to particular orientations (describing joint sets) and 3D surfaces (describing faults or dykes). The output of the method represents two catalogues-one for seismic and another for aseismic events. The catalogues contain origin times (mining steps), coordinates, seismic potencies and source mechanisms of events. The method was applied to real case studies at two mines. The Boundary Element Method (BEM) models of the mines were of the order of 200,000 Fictitious Force (FF) elements. The first model included seven mining steps and the second model consisted of 33 steps. The computations took seven and 50 hours respectively on a multi-core machine. In the first case the resulting catalogue includes more than 900 seismic events, whereas in the second case the catalogues contained approximately 3,400 seismic and 100 aseismic events. The modelled seismicity may be used as a reference for the interpretation of the observed seismicity. Analysing the discrepancies between the modelled and observed seismicity can also help to assess the validity of input parameters-characteristics of in situ stress and failure criteria. Another possible application of the suggested method is the testing of future mining scenarios. Although the prediction of seismic response for individual scenarios may be not accurate, the difference between the modelled seismicity of the scenarios may help to choose the optimal one in regards to seismic or ground motion hazard.
An approach to assess seismic and ground motion hazard associated with scenario(s) of future mining is suggested. The key element of the approach is the modelling of expected seismicity using the Salamon-Linkov method (Malovichko & Basson 2014) and combining the catalogues of modelled and observed seismic events. The modelling domain is discretised and it is assumed that the potency frequency distribution in each grid point can be described by the Upper Truncated model. The upper cutoff potency Pmax and slope β are presumed the same for all grid points. Pmax is estimated from the combined (observed and modelled) seismic catalogue using record theory (Section 3.4 of Mendecki 2016) and the slope β is evaluated from observed data. The parameter α of the Upper Truncated model is inferred from cumulative potency, which is calculated for each grid point using the combined catalogue of events. The probabilities of occurrence of events exceeding a specific potency are derived for each grid point, assuming that the temporal occurrence of events follows a Poisson distribution. The assessment of ground motion hazard is based on Monte Carlo simulation of ground motion accounting for uncertainties in the Ground Motion Prediction Equation and variation of expected seismicity according to the described above estimate of seismic hazard. There are two utilities of assessment of seismic and ground motion hazard in mines. Firstly, the calculated probabilities can be categorised in terms of the hazard likelihoods specified in the risk assessment matrix established at the mine. This can guide a geotechnical engineer in the required actions. Secondly, the evaluated seismic and ground motion hazard can be rigorously tested after the period of forecast is expired and the actual seismic response to planned mining is recorded. The testing procedures established in crustal seismology can be adopted. The poor performance of the forecast needs to be explained in geomechanical terms and corresponding settings of the modelling of seismicity have to be updated. The suggested approach of forecasting the seismic and ground motion hazard, as well as retrospective testing of the seismic hazard, are illustrated using planned mining sequence and seismic data from Renison mine, Australia.
In May 2002, both active-and passive-source surface wave data were acquired using 4-channel arrays at six selected bridge sites in southeast Missouri. Processing of acquired data (increase of signalto-noise ratio, estimation of phase velocities) was carried out and dispersion curves of Rayleigh wave phase velocities were constructed. Each fundamental mode dispersion curve was then inverted by linearised optimization to a layered shear-wave velocity profile to depths of up to 60 m. The estimated shear-wave velocity profiles were compared to other geotechnical data that had been previously acquired at each test site for the Missouri Department of Transportation (MoDOT) including cone penetrometer test (CPT) data, borehole lithologic control, seismic cone penetrometer test (SCPT) shear-wave data and cross-borehole (CH) shear-wave data. The surface wave models, although smoother than the destructive test logs, are accurate and consistent (17% average difference with CH results on two sites), and, moreover, provide information on lithology above the water table and at depths beyond the SCPT and CH limitations, in a more logistically-easier and costeffective manner.
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