# Digital Processing of Seismic Data from Open-Pit Mining Blasts
###### Abstract  
This article describes an approach of mathematical processing of signals (seismograms) from five blasthole charges from experimental blasting, each 3 m deep, with equal explosive weight (1 kg), and equidistant (3 m) from one other. The seismic explosive waves were measured at a 13 to 25 m distance. This article provides spectral analysis, wavelet analysis, and fractal analysis results. It defines the dependence of dominant frequency and amplitude on the distance to the blast center. According to the experimental data, the dominant frequency is calculated as \(y=1.0262\lambda^{0.262}\) and the amplitude dependency as \(y=18.139\times^{-2.276}\). Furthermore, the analysis shows that 80% of the entire signal is concentrated in half the area of frequency range, i.e., the low frequency zone is of the most interest. This research defines the dependence of distance on the energy value of signal wavelet analysis. It is demonstrated that, according to the experimental data, the 12 th frequency range is closely correlated with the distance values. This article gives the definitions of entropy, correlation dimension, and predictability time. This experiment shows that entropy and correlation dimension decrease but predictability time increases when the distance to the blast center increases. This article also describes the method for determining optimal drilling and blasting parameters, and concludes with the possibility of applying the analytical results to predicting and enhancing drilling and blasting operations. seismomonitoring; parameters of drilling and blasting operations; fractal analysis; spectral analysis; wavelet analysis Article  
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St. Petersburg Mining University, 2, 21 Line of Vasilyevsky Island, 199106 St.  
that apply variable empirical coefficients. An example of the latter is [PERSON]'s approach [11], which is applicable in Russia. The rest of the world uses approaches formulated by USBM and others [12; 13; 14; 15]. Another more modern approach involving ECM (Error Correction Model) focuses on the application of forecast models [16; 17; 18]. These models are based on artificial neural networks (ANNs) and appear to be more precise, easily adjustable, and more predictable, and they avoid strictly formalized mathematical relations. These models consider factors and relationships, the influence of which could be missed in other approaches. These ANN-based models have a range of features and disadvantages. Regarding the features, forecasting only after a certain period of time showed that the stored amount of data is enough for the training and validation of the neural network. While the ANN is being trained, the object remains uncontrollable, without any possibility to obtain the model data. So, practically, it is easier to use empirical equations. The models based on empirical data are especially relevant, but this statement is true only for a certain forecast horizon. Usually, the forecast horizon is determined strictly by the area where the empirical equation coefficients were found. In this regard, it is a relevant task to search for mathematical methods enabling us to find the empirical equation coefficients applicable to blasting process forecast, and to expand the forecast horizon within a short period of time without loss of accuracy. This paper describes how the mathematical analysis of seismograms helps to find the empirical equations and dependences used for calculations. A principal hypothesis of this research is that the application of mathematical methods for signal processing to seismograms can provide detailed information on drilling and blasting operations, applicable to the determination of empirical coefficients in characteristic equations within the time period but not exceeding the system computation cycle.