Automatic classification of seismic events within a regional seismograph network

This paper presents a fully automatic method for seismic event classification within a sparse regional seismograph network. The method is based on a supervised pattern recognition technique called the Support Vector Machine (SVM). The classification relies on differences in signal energy distribution between natural and artificial seismic sources. We filtered seismic records via 20 narrow band-pass filters and divided them into four phase windows: P, P coda, S, and S coda. We then computed a short-term average (STA) value for each filter channel and phase window. The 80 discrimination parameters served as a training model for the SVM. We calculated station specific SVM models for 19 on-line seismic stations in Finland. The training data set included 918 positive (earthquake) and 3469 negative (non-earthquake) examples. An independent test period determined method and rules for integrating station-specific classification results into network results. Finally, we applied the network classification rules to independent evaluation data comprising 5435 fully automatic event determinations, 5404 of which had been manually identified as explosions or noise, and 31 as earthquakes. The SVM method correctly identified 94% of the non-earthquakes and all but one of the earthquakes. The result implies that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of accuracy. The tool helps to reduce the work-load and costs of manual seismic analysis by leaving only a small fraction of automatic event determinations, the probable earthquakes, for more detailed seismological analysis. The self-learning approach presented here is flexible and easily adjustable to the requirements of a denser or wider high-frequency network.Peer reviewe

Optimal configuration of the local Ostrobothnian seismic network OBF

This study simulates automatic event detection and location performance of a micro-earthquake network centered around a site selected for a future power plant in Finland, Fennoscandian Shield. Simulation of the event location capability is based on a relationship derived between event magnitude and maximum detection distance. Azimuthal coverage and threshold magnitude are computed for different station configurations and the results are presented as contour maps. An optimal configuration of ten seismograph stations is proposed for further on-site surveyNon peer reviewe

Optimal configuration of a micro-earthquake network

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Seismicity and Sources of Stress in Fennoscandia

This chapter investigates the Fennoscandian uplift area since the latest Ice Age and addresses the question if glacial isostatic adjustment may influence current seismicity. The region is in an intraplate area, with stresses caused by the lithospheric relative plate motions. Discussions on whether uplift and plate tectonics are the only causes of stress have been going on for many years in the scientific community. This review considers the improved sensitivity of the seismograph networks, and at the same time attempts to omit man-made explosions and mining events in the pattern, to present the best possible earthquake pattern. Stress orientations and their connection to the uplift pattern and known tectonics are evaluated. Besides plate motion and uplift, one finds that some regions are affected stress-wise by differences in geographical sediment loading as well as by topography variations. The stress release in the present-day earthquakes shows a pattern that deviates from that of the time right after the Ice Age. This chapter treats the stress pattern generalized for Fennoscandia and guides the interested reader to more details in the national chapters.Peer reviewe

The 2018 Geothermal Reservoir Stimulation in Espoo/Helsinki, Southern Finland: Seismic Network Anatomy and Data Features

A seismic network was installed in Helsinki, Finland to monitor the response to an similar to 6-kilometer-deep geothermal stimulation experiment in 2018. We present initial results of multiple induced earthquake seismogram and ambient wavefield analyses. The used data are from parts of the borehole network deployed by the operating St1 Deep Heat Company, from surface broadband sensors and 100 geophones installed by the Institute of Seismology, University of Helsinki, and from Finnish National Seismic Network stations. Records collected in the urban environment contain many signals associated with anthropogenic activity. This results in time- and frequency-dependent variations of the signal-to-noise ratio of earthquake records from a 260-meter-deep borehole sensor compared to the combined signals of 24 collocated surface array sensors. Manual relocations of similar to 500 events indicate three distinct zones of induced earthquake activity that are consistent with the three clusters of seismicity identified by the company. The fault-plane solutions of 14 selected ML 0.6-1.8 events indicate a dominant reverse-faulting style, and the associated SH radiation patterns appear to control the first-order features of the macroseismic report distribution. Beamforming of earthquake data from six arrays suggests heterogeneous medium properties, in particular between the injection site and two arrays to the west and southwest. Ambient-noise cross-correlation functions reconstruct regional surface-wave propagation and path-dependent body-wave propagation. A 1D inversion of the weakly dispersive surface waves reveals average shear-wave velocities around 3.3 km/s below 20 m depth. Consistent features observed in relative velocity change time series and in temporal variations of a proxy for wavefield partitioning likely reflect the medium response to the stimulation. The resolution properties of the obtained data can inform future monitoring strategies and network designs around natural laboratories.Peer reviewe

Local seismic network for monitoring of a potential nuclear power plant area

This study presents a plan for seismic monitoring of a region around a potential nuclear power plant. Seismic monitoring is needed to evaluate seismic risk. The International Atomic Energy Agency has set guidelines on seismic hazard evaluation and monitoring of such areas. According to these guidelines, we have made a plan for a local network of seismic stations to collect data for seismic source characterization and seismotectonic interpretations, as well as to monitor seismic activity and natural hazards. The detection and location capability of the network were simulated using different station configurations by computing spatial azimuthal coverages and detection threshold magnitudes. Background noise conditions around Pyhäjoki were analyzed by comparing data from different stations. The annual number of microearthquakes that should be detected with a dense local network centered around Pyhäjoki was estimated. The network should be dense enough to fulfill the requirements of azimuthal coverage better than 180° and automatic event location capability down to ML ∼ 0 within a distance of 25 km from the site. A network of 10 stations should be enough to reach these goals. With this setup, the detection threshold magnitudes are estimated to be ML = −0.1 and ML = 0.1 within a radius of 25 and 50 km from Pyhäjoki, respectively. The annual number of earthquakes detected by the network is estimated to be 2 (ML ≥ ∼ −0.1) within 25 km radius and 5 (ML ≥ ∼−0.1 to ∼0.1) within 50 km radius. The location accuracy within 25 km radius is estimated to be 1–2 and 4 km for horizontal coordinates and depth, respectively. Thus, the network is dense enough to map out capable faults with horizontal accuracy of 1–2 km within 25 km radius of the site. The estimation is based on the location accuracies of five existing networks in northern Europe. Local factors, such as seismic noise sources, geology and infrastructure might limit the station configuration and detection and location capability of the network.This study presents a plan for seismic monitoring of a region around a potential nuclear power plant. Seismic monitoring is needed to evaluate seismic risk. The International Atomic Energy Agency has set guidelines on seismic hazard evaluation and monitoring of such areas. According to these guidelines, we have made a plan for a local network of seismic stations to collect data for seismic source characterization and seismotectonic interpretations, as well as to monitor seismic activity and natural hazards. The detection and location capability of the network were simulated using different station configurations by computing spatial azimuthal coverages and detection threshold magnitudes. Background noise conditions around Pyhäjoki were analyzed by comparing data from different stations. The annual number of microearthquakes that should be detected with a dense local network centered around Pyhäjoki was estimated. The network should be dense enough to fulfill the requirements of azimuthal coverage better than 180° and automatic event location capability down to ML ∼ 0 within a distance of 25 km from the site. A network of 10 stations should be enough to reach these goals. With this setup, the detection threshold magnitudes are estimated to be ML = −0.1 and ML = 0.1 within a radius of 25 and 50 km from Pyhäjoki, respectively. The annual number of earthquakes detected by the network is estimated to be 2 (ML ≥ ∼ −0.1) within 25 km radius and 5 (ML ≥ ∼−0.1 to ∼0.1) within 50 km radius. The location accuracy within 25 km radius is estimated to be 1–2 and 4 km for horizontal coordinates and depth, respectively. Thus, the network is dense enough to map out capable faults with horizontal accuracy of 1–2 km within 25 km radius of the site. The estimation is based on the location accuracies of five existing networks in northern Europe. Local factors, such as seismic noise sources, geology and infrastructure might limit the station configuration and detection and location capability of the network.Peer reviewe

Helsingin seisminen asemaverkko ja seismisyys 2020

Seismologian instituutti perusti yhteistyössä Helsingin kaupungin kanssa vuosina 2019–2020 kolmesta seismisestä havaintoasemasta koostuvan HelsinkiNet-verkon. Verkon asemat toimivat Kuninkaantammessa (KUNI), Lauttasaaressa (LAUT) ja Vuosaaressa (VUOS) sekä valtakunnallisen että pääkaupunkiseudun asemaverkon automaattisten havaintojärjestelmien yhteydessä. Asemien kohinataso osoittautui sijaintiin nähden matalaksi ja pitkiä datakatkoja ei ollut. HelsinkiNet-asemien lisäksi pääkaupunkiseudun länsiosissa toimivat St1:n lämpövoimalahankkeen valvontaan perustetut asemat HEL1-HEL5. Vuonna 2020 vahvistettuja seismisiä tapauksia oli 30 km:n säteellä Rautatientorista 484. Suurin osa niistä oli räjäytyksiä. Indusoituja maanjäristyksiä tapahtui Espoossa erityisesti Otaniemessä mutta myös Koskelossa. Luonnollisia maanjäristyksiä oli kolme, niistä kaksi 0,7 magnitudin tapausta Hakunilassa ja yksi –0,5 magnitudin tapaus Laajarannassa. Vähintään 0,0 magnitudin indusoitujen ja luonnollisten maanjäristysten määrä oli 24. Suurin osa maanjäristysten kansalaishavainnoista liittyi St1:n Otaniemen hankkeeseen.In 2019–2020, the Institute of Seismology set up, in collaboration with the City of Helsinki, a seismic network, HelsinkiNet, consisting of three stations. The stations of the network were in operation in Kuninkaantammi (KUNI), Lauttasaari (LAUT), and Vuosaari (VUOS) in association with the automatic observation systems of Finland and the Helsinki region. The noise level of stations was low considering their locations, and no long interruptions to data delivery have taken place. In addition to HelsinkiNet, stations HEL1-HEL5 established for the monitoring of the St1 deep heat project were operational in the western part of Helsinki region. In 2020, the number of confirmed seismic events within 30 km from the Central Railway Station of Helsinki was 484, most of them explosions. Induced earthquakes happened in Espoo, particularly in Otaniemi, but also in Koskelo. Three natural earthquakes occurred, two of them were 0.7-magnitude events in Vantaa and one was a –0.5-magnitude event in Laajaranta. The number of natural and induced earthquakes of at least magnitude 0.0 was 24. Most of the macroseismic observations of earthquakes were associated with the St1 geothermal plant project in Otaniemi, Espoo

A seismic network to monitor the 2020 EGS stimulation in the Espoo/Helsinki area, southern Finland

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Early epithelial signaling center governs tooth budding morphogenesis

During organogenesis, cell fate specification and patterning are regulated by signaling centers, specialized clusters of morphogen-expressing cells. In many organs, initiation of development is marked by bud formation, but the cellular mechanisms involved are ill defined. Here, we use the mouse incisor tooth as a model to study budding morphogenesis. We show that a group of nonproliferative epithelial cells emerges in the early tooth primordium and identify these cells as a signaling center. Confocal live imaging of tissue explants revealed that although these cells reorganize dynamically, they do not reenter the cell cycle or contribute to the growing tooth bud. Instead, budding is driven by proliferation of the neighboring cells. We demonstrate that the activity of the ectodysplasin/Edar/nuclear factor kappa B pathway is restricted to the signaling center, and its inactivation leads to fewer quiescent cells and a smaller bud. These data functionally link the signaling center size to organ size and imply that the early signaling center is a prerequisite for budding morphogenesis.Peer reviewe