This work presents the methodology for analyzing the impact of ground vibrations induced during the drilling of gas/oil exploration wells on the surrounding constructions, as well as on humans and the natural environment. In the primary stage, this methodology is based on measurements of ground vibrations induced by a specific type of drilling system in the so-called reference site. In the next stage, ground vibrations are estimated in similar conditions to another design site, these conditions are assumed for a given drilling system, treated as a vibration source. In both sites, special seismic and geotechnical data are collected to construct numerical models for dynamic analyses. Finally, if it is required, a protection system is proposed with respect to the drilling technology and local conditions. The methodology presented has been tested on the terrain of an active natural gas mine used as the design site, and located in the southeastern part of Poland. The reference site was placed in the terrain of a working drilling system in similar conditions in the central part of Poland. Based on the results of numerical simulations, one may verify the different locations of the drilling rig in the design site with respect to the existing industrial structure. Due to the hazard from destructive ground vibrations, a certain vibroisolation system was proposed at the design site. Based on the results of numerical simulations one could rearrange the components of the drilling system in order to provide maximum security for the surrounding structures.

The major downside of blasting works is blast vibrations. Extensive research has been done on the subject and many predictors, estimating Peak Particle Velocity (PPV), were published till date. However, they are either site specific or global (unified model regardless of geology) and can give more of a guideline than exact data to use. Moreover, the model itself among other factors highly depends on positioning of vibration monitoring instruments. When fitting of experimental data with best fit curve and 95% confidence line, the equation is valid only for the scaled distance (SD) range used for fitting. Extrapolation outside of this range gives erroneous results. Therefore, using the specific prediction model, to predetermine optimal positioning of vibration monitoring instruments has been verified to be crucial. The results show that vibration monitoring instruments positioned at a predetermined distance from the source of the blast give more reliable data for further calculations than those positioned outside of a calculated range. This paper gives recommendation for vibration monitoring instruments positioning during test blast on any new site, to optimize charge weight per delay for future blasting works without increasing possibility of damaging surrounding structures.

This paper presents the results of the research aimed at improving the accuracy of predictions regarding the maximum values of resultant components for horizontal ground vibration accelerations in areas threatened by induced seismicity. The presented solution proposes a spatial model of the ground vibration attenuation relationship based on the assumptions of the Joyner-Boore model. When performing statistical analyses to verify the models, great emphasis was placed on the correctness of applied estimation methods to meet the assumptions. The starting point for introducing spatiality into the models was the occurrence of spatial autocorrelation of the residual component when estimating the structural parameters of a model with the least-squares method. Spatial interactions were presented using weight matrices, the construction of which was based on the inverse of the distance between units. During the study, it was found that the estimated spatial model of the ground vibration attenuation relationship showed a much better match with empirical data compared to the classical Joyner-Boore attenuation model.

In this paper, an attempt was made to find out two empirical relationships incorporating linear multivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.