# Digital Processing of Seismic Data from Open-Pit Mining Blasts
###### Abstract
This hypothesis is formulated considering a certain level of drilling and blasting automation, when seismogram values are automatically entered into a control system, and the mathematical methods described below are used for data processing algorithms that aim to optimize the operations and provide real-time recommendations on effective parameters of drilling and blasting operations. Therefore, the \"system computation cycle\" means the period, defined as the real time in the automation system. Spectral, wavelet, and fractal methods of analysis are selected as mathematical methods in this research. The purpose of this research is to define mathematical methods enabling prompt seismic blast wave amplitude prediction. In order to forecast the propagation of blast waves, it is necessary to consider two principal values: peak particle velocity (PPV) and frequency. Thus, the principal target of research devoted to the blast wave effect forecast involves the determination of these values [5; 6; 14]. In most cases, the values are predicted based on two or three blasting and drilling parameters (mainly charge mass and well depth), and other parameters (soil characteristics, weather conditions, etc.) are not considered. However, there are a few research papers where the PPV is forecasted based on a large scope of data, for example, the work in [19], which involves 150 parameters based on ANN. Moreover, some research has studied the application of fuzzy logic and genetic algorithms. The most important examples of such research are listed below. The classification and regression tree (CART) method for PPV prediction is proposed in [20]. It uses 51 datasets as the initial ones, and maximum charge per delay and distance from the blast face as the input variables. A dependence relationship between the simple fractal dimension and complexity and the blast wave frequency structure is discussed in [21]. It describes the relationships between the blast wave parameters and multifractal parameters. The process of devising a model for PPV forecasting is described in [22]. It describes an empirical equation and ANN, enabling the authors to forecast the explosion-induced PPV. The results are based on the datasets of 95 values collected from a granite quarry site in Malaysia (95 blasting works were precisely monitored in a granite quarry site in Malaysia). A forecast model for soil vibration assessment based on gene expression programming (GEP) is presented in [23]. The results are based on the datasets of 102 values collected from a granite quarry in Malaysia. The process of ANN modeling for PPV prediction using five input parameters, namely, load, interval, maximum charge per deceleration stage, distance from the face to the observation point, and rock properties (burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation) is described in [24]. The results are based on the datasets of 112 values collected from limestone mines in Iran. The wavelet analysis application for bearings troubleshooting is discussed in [25]. The analysis shows how the signal is decomposed into different frequencies which are analyzed in order to identify various faults and assess the severity level. The spectra analysis, energy wavelet and fractal characteristics of the blast wave obtained experimentally at different distances from the explosion center are discussed in [26]. This research reveals the dependence of dominant frequency, wavelet transform energy, and their nature in low frequency and high frequency areas, as well as correlation dimension on the distance to the explosion center.