| Summary: | In this study earthquake data is analyzed using a newly developed Migration Based Detection (MBD) algorithm (Wagner et al. 2017). A software environment suitable for manual analysis of large quantities of earthquakes (events) detected by the MBD algorithm is set up, and the MBD algorithm is applied to 13 days of seismic data from a network of 26 seismic stations in the geologically complex Hengill-area in southwest Iceland. A total of 859 event detections are produced and manually inspected. Out of these, 483 are considered true and/or uncertain, making the assessed number of false detections about 44%. A subset of 53 well defined true events are selected for event relocation using manual picking of first arrival P-waves. The relocation resulted in a mean difference of roughly 0.6 km for each coordinate in the horizontal plane and about 1.4 km in depth. Results of the study provide reference data that may aid further development of the MBD algorithm, as well as provide some insight into the performance of the MBD algorithm. The software environment tailored for analyzing events detected by the MBD algorithm may be used as a foundation for continued analysis of detected events. I denna studie analyserades jordskalvsdata med hjälp av en nyligen utvecklad 'migration based detection'-algoritm (Wagner et al. 2017). En mjukvarumiljö skräddarsydd för manuell analys av stora kvantiteter av jordskalv detekterade av MBD-algoritmen iordningställdes, varpå MBD-algoritmen sedan applicerades på 13 dagar av seismisk data från ett nätverk av 26 seismiska stationer i det geologiskt sett komplexa Hengill-området i sydvästra Island. Totalt detekterades 859 jordskalv som genomgick manuell analys. Av dessa klassificerades 483 stycken som bekräftade eller troliga jordskalv, vilket resulterar i en uppskattad felmarginal om ca. 44 %. En delmängd om 53 väldefinierade jordskalv valdes ut för noggrannare analys av ursprungsplats och tidpunkt genom manuell plockning av P-fasankomst. Omlokaliseringen resulterade i en genomsnittlig ...
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