Jökull


Jökull - 01.01.2017, Page 9

Jökull - 01.01.2017, Page 9
Zeinab Jeddi et al. et al., 2003; Sturkell et al., 2008). Árnadóttir et al. (2009) suggested that the observed horizontal ve- locities between 1993 and 2004 could be a conse- quence of rheological structure and fast deglaciation rates. Similarly, Spaans et al. (2015) explained the long-wavelength uplift observed with satellite radar interferometry (INSAR) and GPS measurements from 2001 to 2010 as the response to long-term shrinking of the ice cap. SEISMIC NETWORK AND DATA After the 2010 eruption of Eyjafjallajökull, a tempo- rary network of nine stations was deployed on and around Katla volcano in May 2011 until August 2013 to monitor the volcano closely. In addition, the perma- nent monitoring network around Mýrdalsjökull, run by IMO, was expanded to 10 stations. Therefore, a to- tal of 19 seismic stations were operating around Katla between May 2011 and August 2013 (Figure 2). They were equipped with broadband or intermediate-band instruments. Batteries, wind generators and/or solar panels were used as power sources. Details on the instrumentation and data recovery are found in Jeddi et al. (2016). Seismic data were acquired at a 100 Hz sampling rate in continuous mode, but technical problems, often due to harsh weather conditions, es- pecially during winter, caused long station downtime in some cases. A densified seismic network in a seismically ac- tive area like Katla improves the detection level of seismic events. Therefore, we ran an automatic de- tection algorithm developed at IMO (Stefánsson et al., 1993; Bödvarsson et al., 1999) using data from the whole 19-station network. We identified four main seismic clusters in the data. Three of them, i.e. the caldera, the west, and south flank, were already known and included also in the IMO catalog. The fourth cluster, located on the east flank at the tip of Sandfellsjökull (eastern Mýrdalsjökull, Figure 2), is only represented by a few events in the IMO catalog (only 6 events during 2011–2013). This is likely due to the fact that the IMO catalog is based on the per- manent stations only, while our catalog is based also on the temporary ones. In particular, 2 out of the 3 (LOD, KKE, RJU) seismic stations closest to the east- ern events are part of the temporary network (Figure 2). Those stations were crucial to improve the detec- tion of the seismicity in this area. CHARACTERISTICS OF THE EASTERN EVENTS Waveform characteristics The eastern events are low magnitude (ML<1) events with a frequency content in the range 4–25 Hz. Both P- and S-wave arrivals are clear. They can, therefore, be classified as VT events (McNutt, 2005). Based on visual examination of the detected events and similar- ity between waveforms, we identified two families of events (Figure 3). Even though the frequency con- tent of the two families is similar, their waveforms are different, e.g., the amplitude ratio between P and S phases is very different on different components. Looking at vertical components of the waveforms, the P-phase has much lower amplitude than the S in fam- ily 1, while for family 2 the opposite is the case. This suggests that the source mechanisms of the two fami- lies are different. Temporal evolution Because the waveforms have similar appearance, we were able to improve the event detection us- ing a cross-correlation method. We set up a cross- correlation scheme between a reference event and the continuous recordings (see Lindblom et al. (2015) for a detailed explanation of the cross-correlation method). The event with highest signal to noise ra- tio was chosen as a template for each family (family 1: 6th December, 2011 at ∼19:44:42; family 2: 5th December, 2011 at ∼09:19:34). The waveforms were filtered between 3 and 20 Hz and a window (∼2 s) that included both P and S phases was cross-correlated with the continuous data from July 2011 to August 2013. We ran this process using data from the best available station at any given time. We used LOD (where the waveforms have the highest signal to noise ratio) between September 2011 and August 2013, and KKE between July and September 2011. With this process we were able to detect 301 events, 270 belonging to family 1 and 31 to family 4 JÖKULL No. 67, 2017

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