Automatic seismic event detection using migration and stacking: a performance and parameter study in Hengill, southwest Iceland. Issue 3 (24th March 2017)

• Record Type: Journal Article
• Authors: Wagner, F.; Tryggvason, A.; Roberts, R.; Lund, B.; Gudmundsson, Ó.
• Is Part Of: Geophysical journal international. Volume 209:Issue 3(2017:Jun.)
• Page Start: 1866
• Page End: 1877
• Publication Date: 2017-03-24
• Subjects: Numerical solutions -- Time-series analysis -- Induced seismicity -- Volcano seismology; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggx127
• Languages: English
• ISSNs: 0956-540X

Abstract

Summary: We investigate the performance of a seismic event detection algorithm using migration and stacking of seismic traces. The focus lies on determining optimal data dependent detection parameters for a data set from a temporary network in the volcanically active Hengill area, southwest Iceland. We test variations of the short-term average to long-term average and Kurtosis functions, calculated from filtered seismic traces, as input data. With optimal detection parameters, our algorithm identified 94 per cent (219 events) of the events detected by the South Iceland Lowlands (SIL) system, that is, the automatic system routinely used on Iceland, as well as a further 209 events, previously missed. The assessed number of incorrect (false) detections was 25 per cent for our algorithm, which was considerably better than that from SIL (40 per cent). Empirical tests show that well-functioning processing parameters can be effectively selected based on analysis of small, representative subsections of data. Our migration approach is more computationally expensive than some alternatives, but not prohibitively so, and it appears well suited to analysis of large swarms of low magnitude events with interevent times on the order of seconds. It is, therefore, an attractive, practical tool for monitoring of natural or anthropogenic seismicity related to, for example, volcanoes, drilling or fluid injection.