Summary Traditional earthquake location relying on first arrival picking is challenging for microseismic events with low signal-to-noise ratio. Over the past years, alternative procedures have been explored based on the idea of migrating the energy of an earthquake back into its source position by stacking along theoretical traveltime curves. To avoid destructive interference of signals with opposite polarity, it is common to transform the input signals into positive timeseries. Stacking-based source location has been successfully applied at various scales, but existing studies differ considerably in the choice of characteristic function, the amount of pre-processing and the phases used in the analysis. We use a dataset of 62 natural microearthquakes recorded on a 2D seismic array of 145 vertical geophones across the glacially-triggered Burträsk fault to compare the performance of five commonly used characteristic functions: the noise filtered seismograms and the semblance, the envelope, the short term average/long term average ratio and the kurtosis gradient of the seismograms. We obtain the best results for a combined P- and S-wave location using a polarity-sensitive characteristic function, i.e., the filtered seismograms or the semblance. In contrast, the absolute functions often fail to align the signals properly, yielding biased location estimates. Moreover, we observe that the success of the procedure is very sensitive to noise suppression and signal shaping prior to stacking. Our study demonstrates the usefulness of including lower quality S-wave data to improve the location estimates. Furthermore, our results illustrate the benefits of retaining the phase information for location accuracy and noise suppression. To ensure optimal location results, we recommend carefully pre-processing the data and test different characteristic functions for each new dataset. In spite of the sub-optimal array geometry, we obtain good locations for most events within ∼30-40 km of the survey and the locations are consistent with an image of the fault trace from an earlier reflection seismic survey.

<p>Soon after midnight on 26 September 2022 the Swedish National Seismic Network, using data from Sweden, Denmark and Germany, automatically detected a seismic event in the Baltic southeast of the Danish island of Bornholm. The event was followed 17 hours later by a second, more complex, event northeast of Bornholm. The automatic locations of the events were within 6-9 km of later reported gas leaks in the Nord Stream 1 and 2 pipelines. Using recently developed, machine learning based, classifiers both events were automatically classified as explosions. Subsequent analysis of the second event revealed that it was in fact two blasts, separated by about 7 seconds. As the events occurred in the transition zone between the Fennoscandian Shield and the younger terranes of Denmark and northern Germany, 3D tomographic P- and S-velocity models were developed to improve locations and assess uncertainties, bringing the locations closer to the pipelines. Spectral analysis of the blast data show clear reverberations consistent with underwater explosions and a blast depth of approximately 75 m. The conclusion that the events are underwater blasts are further supported by data on known underwater explosions and a few earthquakes in the area. The magnitude of the first event was estimated at ML 1.9 and the combined second and third event had ML 2.3. Estimating the equivalent yield in the explosions is, however, non-trivial. Comparison to ground truth underwater explosions suggests yields of one to a few hundred kilos of equivalent TNT. The contribution to the seismic energy from suddenly outflowing methane gas is under investigation and results will be included in the presentation.</p>

<p>Distinguishing small earthquakes from man-made blasts at construction sites, in quarries and in mines is a non-trivial task during automatic event analysis and thus typically requires manual revision. We have developed station-specific classification models capable of both accurately assigning source type to seismic events in Sweden and filtering out spurious events from an automatic event catalogue. Our method divides all three components of the seismic records for each event into four non-overlapping time windows, corresponding to P-phase, P-coda, S-phase and S-coda, and computes the Root-Mean-Square (RMS) amplitude in each window. This process is repeated for a total of twenty narrow frequency bands. The resulting array of amplitudes is passed as inputs to fully connected Artificial Neural Network classifiers which attempt to filter out spurious events before distinguishing between natural earthquakes, industrial blasts and mining-induced events. The distinction includes e.g. distinguishing mining blasts from mining induced events, shallow earthquakes from blasts and differentiating between different types of mining induced events. The classifiers are trained on labelled seismic records dating from 2010 to 2021. They are already in use at the Swedish National Seismic Network where they serve as an aid to the routine manual analysis and as a tool for directly assigning preliminary source type to events in an automatic event catalogue. Initial results are promising and suggest that the method can accurately distinguish between different types of seismic events registered in Sweden and filter out the majority of spurious events.</p>

<p>Two clear seismic events were observed on 26<sup>th</sup> September 2022 associated with the reported leaks from the Nord Stream 1 (Event 1, NE of Bornholm) and Nord Stream 2 pipelines (Event 2, SE of Bornholm). Arrivals of both events were detected and associated using data from several arrays in Norway and Finland, including the IMS stations NOA, FINES and ARCES. Additional signal analysis with data from the Swedish National Seismic Network and the Danish station on Bornholm enabled a third event to be identified. Auto-correlation analysis of the Event 2 revealed the third event (Event 2B) about 7 seconds after the main amplitude of the P onset (Event 2A). In contrast, for Event 1 SE of Bornholm no additional events could be identified from auto-correlation analysis, which increases confidence that these additional arrivals are not caused by interaction with geological structures. We also observe an arrival 7 s after the Pn phase before the Pg arrival on the NORES array. However, we cannot exclude that this onset interferes with the arrival of the PnPn phase. We then use the time differences between Event 2A and 2B measured by auto-correlation analysis on the Swedish and Danish network stations to determine relative epicentre locations. The results suggest that the two overlapping events occurred just about 220 m apart from each other. The relative locations fit very well with the distance between both pipelines of Nord Stream 1 at the Westernmost gas plume location (NE of Bornholm). We also estimated preliminary full moment tensors for Event 1 and 2 using seismic waveform data and analysed them on a source-type diagram. The results show positive isotropic parameters consistent with explosion-type mechanisms.</p>

<p>In recent years the Swedish National Seismic Network (SNSN) made an increased effort to modernize station and communication equipment, and thereby has significantly improved continuous real-time data availability and data quality.  Currently, the SNSN is operating 67 permanent broadband seismic stations evenly distributed in the South, along the Eastern shore and the North of Sweden. In addition, a temporay network of 13 stations was deployed in 2021 for a 3-year period to monitor small earthquakes associated with a linear cluster of events at the Western cost of the Gulf of Bothnia. SNSN transmits continuous real-time data to networks in the neighboring countries (Norway, Denmark, Finland, Germany) and in turn receives and processes data from about 120 stations abroad.</p> <p>Compared to many other countries, Sweden has a relatively low seismicity. This makes it all the more important to focus on small seismic events in order to map crustal structures and processes and to provide a data basis for reasonable long-term seismic hazard assessments. Turning to small events means to deal with many events and most of them being man-made seismic events (blasts related to quarries, underground mines, road/tunnel constructions, etc). Within the last 22 years SNSN has recorded and analyzed about 170,000 seismic events out of which only 11,000 (~6.5%) were classified as natural events. Automatic event processing and event type classification are of the essence in order to cope with the amount of data and to decrease the workload of the analysts.</p> <p>SNSN is running four different and independent automatic processing routines in parallel: SeisComp (SC), Earthworm (EW), MSIL and a migration stack algorithm (MS). The main purpose of SC and EW is to detect and locate events in realtime. Both systems are set up to be very sensitive in order to detect as small events as possible, which on the other side also increases the probability to generate spurious events. To counterbalance that we generate a common event catalogue (i.e. events that were located both by SC as well as EW) which turnes out to be very reliable. The common event bulletin captures events as small as about ML1 and contains almost all events ML > 1.5.  MSIL and MS are running in offline and delayed mode which allows the backfilling of potential data gaps, before processing. These systems are catching events down to about magnitude ML0. All events of the common bulletin and the MSIL bulletin are subject to an automatic Neural Network event typ classification into earthquakes, blasts and mining-induced events. In a final step all events classified as earthquakes, significant blasts (felt events or events of special interest) or events with unclear cassification are reviewed by SNSN analysts and are being made available on the SNSN web page. In the framework of EPOS-Sweden, SNSN will make available the waveform and metadata data of the permanent network via FDSN-services.</p>