<p>At Geldinganes Island, Reykjavik, Iceland a hydraulic stimulation was recently&#160;conducted to enhance the productivity of an existing hydrothermal well. An&#160;experimental cyclic soft stimulation concept was applied. Seismic risk was&#160;assessed with an appropriate monitoring network which was set up and operated&#160;before, during, and for some time after the stimulation activities. An advanced&#160;traffic light system was developed and operated for the first time in this&#160;setup.</p><p>A crucial element in such traffic light systems is the real-time monitoring of&#160;background and induced seismicity. During the experiment, real-time seismograms&#160;from the monitoring network were streamed over the internet to three different&#160;institutions (ISOR, ETHZ and GFZ), where they were analysed independently, with&#160;different combinations and setups of automatic, semi-automatic and manual&#160;methods. Both, classic pick based approaches and modern full-waveform methods&#160;were applied. Locations, magnitudes, and centroid moment tensor solutions were&#160;determined.</p><p>Many things can go wrong in real-time or near-real-time processing of seismic&#160;data. Sensor failures, transmission failures, timing issues, processing&#160;hardware failures, computational limitations, software bugs and human error,&#160;just to name a few. In a temporary network the challenges are additionally&#160;salted by the need to validate sensor responses, orientations, gain factors and&#160;site conditions in a short time frame between station setup and beginning of&#160;the experiment. Furthermore, tuning of advanced analysis methods can be&#160;difficult without example events at hand.</p><p>In this contribution, we would like to share our lessons learned in&#160;near-real-time processing of data from a heterogeneous temporary seismic&#160;network.&#160;</p>
AN APPROACH FOR REDUCING CO2 EMISSION BY REAL TIME TRAFFIC LIGHT CONTROL USING IOT