In this article, we consider the features of the perception of student information in science, technology, engineering, and mathematics (STEM) education, in order to draw the attention of researchers to the topic of learning in practice through field training. The article shows the results of these studies in Russia and the Commonwealth of Independent States (CIS countries: Armenia, Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, and Uzbekistan, as an example) to reflect the global trends. For this purpose, we examined the expectations of students in Russia and the CIS countries from training related to lectures and field training. We created a questionnaire and distributed it in three Moscow-based universities (Moscow State University of Geodesy and Cartography—MIIGAiK, Moscow Aviation Institute—MAI, and Moscow City University—MCU). Our key assumption is that field practices in Russian universities are qualitatively different from the phenomenon described in European literature, where digital or remote field practices have already emerged. The results obtained through the survey show the tendency of students’ perceptions to fulfill practical duties (in a laboratory with instruments of field training) in STEM education.

The Mikhnevo small-aperture array (SAA) was designed as an instrument for various regional seismic studies, including the compilation of a detailed catalogue of industrial blasts in the East European craton. This array includes 12 observation points arranged in three circles and equipped with SM3-KV shortperiod seismometers. The Institute of Geospheres Dynamics launched the array in 2004, and since that time it has been detecting up to 1000 industrial blasts per year. The Mikhnevo SAA uses beam formation for array processing. The stacking of individual waveforms reduced to a reference point allows the suppression of microseismic noise and improving the signal-to-noise ratio (SNR) relative to a three-component station. An improved SNR for a given signal is equivalent to a reduced detection threshold: much weaker signals can be detected with the use of a beam-formation technique. In turn, much more signals from small industrial explosions are detected. Weak signals are difficult to identify because of the higher uncertainty in the estimates of such characteristics as azimuth, slowness, and amplitude. Having a ten-year catalog of industrial blasts and the archive of raw digital records for this period, we apply a waveform cross-correlation (matched filter) technique, which has an extremely high relative location accuracy and thus identification capability. We have created a set of master events with relevant waveform templates for automatic data processing and creation of an accurate catalogue of industrial blasts.