@inproceedings{epos1977,
       booktitle = {35th General Assembly of the European Seismological Commission},
           month = {September},
           title = {Automated detection and location of picoseismicity of hydraulic
fracturing experiment using continuous waveforms},
          author = {Jose Angel Lopez-Comino and Sebastian Heimann and Simone Cesca and Claus Milkereit and Torsten Dahm and Arno Zang},
            year = {2016},
             url = {https://episodesplatform.eu/eprints/1977/},
        abstract = {The geothermic Fatigue Hydraulic Fracturing (FHF) in situ underground experiment (Nova project 54-14-1)
took place in the {\"A}sp{\"o} Hard Rock Laboratory, Sweden. The basic idea was to validate the FHF concept under
controlled conditions at the -410 depth level. Conventional versus cyclic, and pulse hydraulic injection schemes
were monitored with three different monitoring arrays (Acoustic Emission [AE], Microseismic Monitoring and
Electro-Magnetic) in the near- and far-?eld. The network was designed for high sensitivity in order to observe
very small seismic events. The AE sensors allow for sensitive recording in the frequency range 1 to 100 kHz; the
system measuring is capable to operate in trigger and continuous mode with a sampling of 1 MHz. In this work
we consider continuous recordings and apply recently developed automated full waveform detection and location
algorithms which are based on the stacking of characteristic functions calculated from squared amplitudes.
We signi?cantly increase the detection rate in comparison to trigger mode routines, because overlapping and
weak events are resolved with our method. However, the location accuracy is not as high since waveforms are
low pass ?ltered. Most detection concentrated during the ?uid injection occurred around the fracking stages.
Frequency-magnitude distribution characteristics are investigated using a magnitude scale estimated from the
amplitude recorded at AE sensors. We demonstrate that the stacking of characteristic functions yields to a
signi?cant improvement of the detection and location also in presence of noisy records, supporting the adoption
of similar techniques for other induced and natural seismic activity monitoring systems.

This work is funded by the EU H2020 SHEER project. Nova project 54-14-1 was ?nancially supported by
the GFZ German Research Center for Geosciences (75\%), the KIT Karlsruhe Institute of Technology (15\%) and
the Nova Center for University Studies, Research and Development (10\%). An additional in-kind contribution of
SKB for using {\"A}sp{\"o} Hard Rock Laboratory as test site for geothermal research is greatly acknowledged.}
}