# Detecting Atypical Values and Their Influence on Blast-Induced Seismic Measurement Results
###### Abstract  
Blasting is an essential part of any mining or civil engineering project along with all the benefits that it brings, such as cost and time effectiveness, and safety. Still, there are a few downsides to blasting. Ground oscillation velocity as the most significant impact of blasting has been studied broadly. However, not all measured values should be used for PPV (peak particle velocity) predictor or model development. If a false measured value is included in the model or predictor development, it will provide erroneous results that can lead to the damage of the surrounding structures or an increase in the cost of blasting works. There is no clearly defined procedure for separating atypical values (outliers) within blast-induced seismic-effects measurement data. This paper recommends how to properly validate vibration velocity data by detecting and excluding atypical values and how it influences blast-induced seismic measurement results.  
atypical values; blast-induced vibrations; charge weight per delay; peak particle velocity 1  
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Nowadays, most of the published articles on the subject of blast-induced seismic effects are founded on a significant quantity of measurement data compiled during the particular project or quarry development and analyzed through statistical methodology or ANN (Artificial Neural Network) [16; 17]. Substantial amounts of open-pit blasting works measurements can be compiled to obtain an attenuation curve. Civil works generally lack this type of measurement data; therefore, the assessment of the first test blast is vital for acquiring an adequate amount of data for the safe execution of blasting works [18]. The uncertainty in measurements has been researched in relation to the horizontal orientation of the triaxial geophone and coupling method [19]. There are guidelines in which the requirements for measuring instruments (seismographs) and the method for installation during measurement are described [20]. However, not all measurement data are eligible for calculations. Errors during measurement should be avoided as much as possible [19]. In many different fields, methods for the detection of atypical values have already been accepted [21; 22; 23]. Different fields use different confidence intervals for atypical value detection [24]. In blasting, confidence intervals are used to provide the certainty of the calculated PPV (peak particle velocity) prediction results. However, there is no reference to any kind of atypical value detection in blast-induced seismic measurement data.  
The test blast measurements used in this paper are performed for several parallel studies. First, we analyze the benefit of using a larger number of instruments in one measurement line [25], followed by measurement instruments positioned at distances that are of relevance; thus, the data is not extrapolated, because results achieved using extrapolation are at least questionable, if not erroneous [26]. This study is concerned with errors in the measurement data and how to detect them. There are three types of errors: gross, systematic, and random. Gross errors mainly cover human mistakes in reading instruments and recording and calculating measurement results. Systematic errors can be instrumental, environmental, and observational. Random errors are caused by happenings or disturbances about which we are unaware. Studied errors fall under the random errors category, which unfortunately cannot be prevented. Therefore, this research recommends how to detect and exclude atypical values (outliers) and clarifies what influence they have on blast-induced seismic measurement results.  
The paper is divided in two parts. The first part and the main hypothesis include finding the optimal tool to detect atypical values in blast-induced seismic measurement data. The second part is the verification of results through the blast-induced analysis software \"Blastware\" from Instantel, Ottawa, ON, Canada.