Identification of seismic events from continuously recorded seismic data in real-time through a Digital Seismic Data Recording system is a difficult task. Despite the vast amount of research in this field, the signal processing and event parameters discrimination algorithms have not yet fully come of age. Presently, we have a wide spectrum of trigger algorithms, ranging from a very simple amplitude threshold type to the sophisticated ones based on pattern recognition approaches. Some of the other approaches use adaptive technique and neural network methods. Researchers are continuously making efforts for the development of algorithms using various techniques, which produce minimum false trigger. Some approaches have been reported which are accurate for detecting first phase of events and take minimum possible computational time. In this paper several approaches for detecting event signals in background noise are presented and their precision evaluation is discussed.

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Rock breakage by explosives is followed by throw or heaving the broken material and occasional flyrock. Heaving is a desired feature of blasting for efficient mucking. However, flyrock is a rock fragment that travels beyond the designated distance from a blast in surface mines, and poses a threat to adjacent habitats. Here, we decipher the importance and sensitivity of the variables and factors used to establish the predictive regime of throw with more emphasis on flyrock. The data collected were modelled using artificial neural network approach. The importance and sensitivity of variables and factors were delineated so that they are in tune with the rationale of the outcome of the blast. A combinatory approach was devised to arrive at minimal variables and factors to reduce the statistical redundancy, and to propose a rational predictive regime for throw and flyrock in surface mines.Keywords: Artificial neural network, blasting, flyrock, throw, surface mines.BLASTING is an integral part of excavation in mines and continues to be a major method of rock fragmentation due to the economy of operation. Blasting, in addition to fragmentation, is associated with throwing the muck generated, vibrations, air overpressure and flyrock. While fragmentation and throw are desired effects, flyrock is an undesirable outcome. Flyrock is a fragment of rock that travels greater distances than desired, in comparison to throw which is limited to a few multiples of bench height. Flyrock is not only a threat to nearby habitats, but poses a challenge to miners as all sorts of 'Objects of Concern ' (OC) 1 are affected by it. Flyrock is one of the major causes of blast induced fatalities and accidents 2 . There are several reasons for flyrock which belong to the domain of rockmass including structural discontinuities 3 , blast design and explosive variables. Several attempts were made by different authors to identify the reasons for flyrock and several equations have been proposed to predict flyrock distance. However, there is a disparity between cause of flyrock and the variables identified that have been used in prediction regime 4 . Such a disparity is reflected in Tables 1 and 2 and a comparison is shown in Figure 1. RESEARCH COMMUNICATIONS CURRENT SCIENCE, VOL. 111, NO. 9, 10 NOVEMBER 2016 1525 A similar compilation of variables used in models that predict distances travelled by flyrock is shown in Table 2.As seen in Table 2, variables namely stemming length, blasthole depth, specific charge, burden and blasthole diameter emerge as the principal ones in predicting the distance which a flyrock can travel.A comparison of top seven causative factors (Table 1), and those used in predictive equations (Table 2) are given in Figure 1.From Figure 1 we infer that principally two variables, namely burden and rock, differ with regard to cause and prediction, probably due to the difficulty in assessing rock mass and burden. Other variables closely follow each other in cause and prediction citations, establishing their importance.Acco...

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