Comparison of surface and borehole seismic network performances in observing induced seismicity from a deep EGS stimulation

During June and July of 2018 St1 Deep Heat Oy (ST1DH) performed a hydraulic stimulation between 6 km and 7 km depth beneath the Aalto University campus in Otaniemi, Espoo, Finland to establish an Enhanced Geothermal System (EGS) for district heating. The area surrounding the EGS is among the most densely populated areas in Finland with downtown Helsinki located only ~6 kilometers away.

The Institute of Seismology, University of Helsinki (ISUH) monitored the stimulation using a network of surface seismic stations and geophones. ISUH operates a temporary network of 5 broadband stations recording at 250 Hz in the Helsinki and Espoo region within ~10 km of the EGS well. During the stimulation and the immediate post-stimulation stage, ISUH also operated a temporary ~100 geophone network. This network consisted of three-component 4.5 Hz PE-6/B-geophones connected to DATA-CUBE3 digitizers recording at 400 Hz. The geophones were organized in 3 large arrays consisting of ~25 stations, 3 small 4-station arrays, and 8 single stations. ISUH was also granted access to data from borehole stations installed by ST1DH. These 12 semi-permanent borehole seismometers were installed at depths between 238 m and 1620 m and registered at 500 Hz. 

Our goal is to explore the performances of the simultaneously operating surface and borehole networks in monitoring induced seismicity in an urban hard rock environment with comparatively low attenuation of seismic signals. The results can be used in planning and designing future acquisition and monitoring systems around natural laboratories in similar envrionments.

For this we analyze the induced event detection capability based on data from the surface broadband and geophone stations and compare it to the data collected by the borehole sensors. First, the regular tools used in ISUH routine automatic analysis are applied to borehole station data to form the baseline for detection capability. We then apply the same procedures to the surface data where we take advantage of the geophone arrays by utilizing beamformed stacks in order to enhance the quality of automatic detection by improving the signal-to-noise ratio (SNR) of induced events. Second, we compile statistics of the residuals of a dataset of manually refined picks and the automatic detections to evaluate systematic effects or biases.  Third, we apply a detection and picking routine from the literature to ~500 event traces recorded at a borehole station and a colocated 25 sensor array to form a consistent data base for comparison with the routine ISUH picker. We explore the detection capability of beamformed surface record stacks by evaluating the SNR and detection statistics compared to the single borehole station. We focus on the effect of the number of traces per stack and on the frequency dependent diurnal and weekly noise variations associated with the urban environment.