@ARTICLE{Tomaszewski_Dariusz_Switching_2022,
 author={Tomaszewski, Dariusz and Rapiński, Jacek and Stolecki, Lech and Śmieja, Michał},
 volume={vol. 67},
 number={No 2},
 journal={Archives of Mining Sciences},
 pages={317-332},
 howpublished={online},
 year={2022},
 publisher={Committee of Mining PAS},
 abstract={Contemporary mine exploitation requires information about the deposit itself and the impact of mining activities on the surrounding surface areas. In the past, this task was performed using classical seismic and geodetic measurements. Nowadays, the use of new technologies enables the determination of the necessary parameters in global coordinate systems. For this purpose, the relevant services create systems that integrate various methods of determining interesting quantities, e.g., seismometers / GNSS / PSInSAR. These systems allow detecting both terrain deformations and seismic events that occur as a result of exploitation. Additionally, they enable determining the quantity parameters that characterise and influence these events. However, such systems are expensive and cannot be set up for all existing mines. Therefore, other solutions are being sought that will also allow for similar research. In this article, the authors examined the possibilities of using the existing GNSS infrastructure to detect seismic events. For this purpose, an algorithm of automatic discontinuity detection in time series “Switching Edge Detector” was used. The reference data were the results of GNSS measurements from the integrated system (seismic / GNSS / PSInSAR) installed on the LGCB (Legnica-Głogów Copper Belt) area. The GNSS data from 2020 was examined, for which the integrated system registered seven seismic events. The switching Edge Detector algorithm proved to be an efficient tool in seismic event detection.},
 type={Article},
 title={Switching Edge Detector as a Tool for Seismic Events Detection Based on GNSS Timeseries},
 URL={http://www.czasopisma.pan.pl/Content/123709/PDF/Archiwum-67-2-08-Tomaszewski.pdf},
 doi={10.24425/ams.2022.141461},
 keywords={GNSS, seismology, switching edge detection, timeseries analysis},
}