Automatic data processing and analysis system for monitoring region around a planned nuclear power plant

The Institute of Seismology of University of Helsinki is building a new local seismic network, called OBF network, around planned nuclear power plant in Northern Ostrobothnia, Finland. The network will consist of nine new stations and one existing station. The network should be dense enough to provide azimuthal coverage better than 180° and automatic detection capability down to ML −0.1 within a radius of 25 km from the site.The network construction work began in 2012 and the first four stations started operation at the end of May 2013. We applied an automatic seismic signal detection and event location system to a network of 13 stations consisting of the four new stations and the nearest stations of Finnish and Swedish national seismic networks. Between the end of May and December 2013 the network detected 214 events inside the predefined area of 50 km radius surrounding the planned nuclear power plant site. Of those detections, 120 were identified as spurious events. A total of 74 events were associated with known quarries and mining areas. The average location error, calculated as a difference between the announced location from environment authorities and companies and the automatic location, was 2.9 km. During the same time period eight earthquakes between magnitude range 0.1–1.0 occurred within the area. Of these seven could be automatically detected. The results from the phase 1 stations of the OBF network indicates that the planned network can achieve its goals.Abstract. The Institute of Seismology of University of Helsinki is building a new local seismic network, called OBF network, around planned nuclear power plant in Northern Ostrobothnia, Finland. The network will consist of nine new stations and one existing station. The network should be dense enough to provide azimuthal coverage better than 180° and automatic detection capability down to ML −0.1 within a radius of 25 km from the site. The network construction work began in 2012 and the first four stations started operation at the end of May 2013. We applied an automatic seismic signal detection and event location system to a network of 13 stations consisting of the four new stations and the nearest stations of Finnish and Swedish national seismic networks. Between the end of May and December 2013 the network detected 214 events inside the predefined area of 50 km radius surrounding the planned nuclear power plant site. Of those detections, 120 were identified as spurious events. A total of 74 events were associated with known quarries and mining areas. The average location error, calculated as a difference between the announced location from environment authorities and companies and the automatic location, was 2.9 km. During the same time period eight earthquakes between magnitude range 0.1–1.0 occurred within the area. Of these seven could be automatically detected. The results from the phase 1 stations of the OBF network indicates that the planned network can achieve its goals.Peer reviewe

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 a micro-earthquake 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

The Finnish National Seismic Network : Toward Fully Automated Analysis of Low‐Magnitude Seismic Events

We present an overview of the seismic networks, products, and services in Finland, northern Europe, and the challenges and opportunities associated with the unique combination of prevailing crystalline bedrock, low natural intraplate seismic background activity, and a high level of anthropogenic seismicity. We introduce national and local seismic networks, explain the databases, analysis tools, and data management concepts, outline the Finnish macroseismic service, and showcase data from the 2017 M 3.3 Liminka earthquake in Ostrobothnia, Finland.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

HelsinkiNet - a collaborative seismological research network of the Institute of Seismology, and the City of Helsinki

Non peer reviewe

HelsinkiNet - a collaborative seismological research network of the Institute of Seismology, and the City of Helsinki

Non peer reviewe

Helsingin seisminen asemaverkko ja seismisyys 2019

Helsinkiin on vuoden 2019 aikana suunniteltu kolmen aseman seismistä verkkoa, HelsinkiNet:iä, joka täydentää Suomen kansallista seimistä verkkoa Helsingissä ja sen lähialueilla. HelsinkiNet:in havaintojen avulla alueelta voidaan havaita pienempiä seismisiä tapauksia kuin aiemmin ja tapaukset voidaan paikantaa tarkemmin. Verkon suunnitelluilla asemapaikoilla on suoritettu testimittauksia syksyllä 2019. Verkko rakennetaan valmiiksi vuonna 2020. Helsingin Yliopiston Seismologian instituutti kerää asemien rekisteröimän aineiston ja paikantaa alueen tapaukset automaattisesti. Instituutti analysoi manuaalisesti maanjäristykset tai muuten merkittävät tapaukset ja tiedottaa niistä tarvittaessa Helsingin kaupungille. Vuonna 2019 Seismologian instituutti on analysoinut Helsingistä ja sen lähialueilta yhden maanjäristyksen, yhden indusoidun maanjäristyksen sekä 299 räjäytystä. Maanjäristys (ML0.9, 59.952°N, 24.967 °E) tapahtui 9.7.2019 merellä, lähellä Helsingin majakkaa. Indusoitu järistys (ML0.6, 60.191°N, 24.841 °E) tapahtui 9.5.2019 liittyen ST1:n geotermisen energian voimalaitoksen rakentamiseen. Liitteessä 1 kerrotaan Helsingin ja sen lähialueiden seismisyydestä ja kaupungissa vuoden 1829 jälkeen havaituista maanjäristyksistä.A three-station seismic network, HelsinkiNet, has been planned to Helsinki during 2019. The network will complement the Finnish National Seismic Network in the Helsinki region, lowering the detection treshold and improving location accuracy of seismic events. During autumn 2019 test measurements were conducted at the planned station locations. The network will be built during 2020. Institute of Seismology of the University of Helsinki (ISUH) will gather data from the stations, and perform automatic event detection. ISUH will analyse earthquakes and other significant events manually, and inform Helsinki City when necessary. In 2019 ISUH has analysed one earthquake, one induced earthquake, and 299 explosions from the Helsinki area. The earthquake (ML0.9, 59.952°N, 24.967 °E) occurred on 9th July under the sea, nearHelsinki lighthouse. The induced earthquake (ML0,6, 60.191°N, 24.841 °E) occurred on 9th May in Otaniemi, probably due to ST1 geothermal plant construction. In appendix 1 (Liite1) seismicity in Helsinki, and earthquake observations by residents there since 1829 are described