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Single Layer Recurrent Neural Network for detection of local swarm-like earthquakes-the application
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SYSNO ASEP 0508961 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Single Layer Recurrent Neural Network for detection of local swarm-like earthquakes-the application Author(s) Doubravová, Jana (GFU-E) ORCID, RID
Horálek, Josef (GFU-E) ORCID, RIDSource Title Geophysical Journal International - ISSN 0956-540X
Roč. 219, č. 1 (2019), s. 672-689Number of pages 18 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords neural networks ; fuzzy logic ; time-weries analysis ; earthquake source observation Subject RIV DC - Siesmology, Volcanology, Earth Structure OECD category Volcanology R&D Projects GA18-05053S GA ČR - Czech Science Foundation (CSF) LM2015079 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) EF16_013/0001800 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support GFU-E - RVO:67985530 UT WOS 000484124800041 DOI 10.1093/gji/ggz321 Annotation We present results of applying a local event detector based on artificial neural networks (ANNs) to two seismically active regions. The concept of ANNs enables us to recognize earthquake-like signals in seismograms because well-trained neural networks are characterized by the ability to generalize to unseen examples. This means that once the ANN is trained, in our case by few tens to hundreds of examples of local event seismograms, the algorithm can then recognize similar features in unknown records. The detailed description of the single-station detection, design and training of the ANN has been described in our previous paper. Here we show the practical application of our ANN to the same seismoactive region we used for its training, West Bohemia/Vogtland (border area Czechia-Saxony, local seismic network WEBNET), and to different seismogenic area, Reykjanes Peninsula (South-West Iceland, local seismic network REYKJANET). The training process requires carefully prepared data set which is preferably achieved by manual processing. Such data were available for the West Bohemia/Vogtland earthquake-swarm region, so we used them to train the ANN and test its performance. Due to the absence of completely manually processed activity for the Reykjanes Peninsula, we use the trained ANN for swarm-like activity in such a different tectonic setting. The application of a coincidence of the single-station detections helps to reduce significantly the number of undetected events as well as the number of false alarms. Setting up the minimum number of stations which are required to confirm an event detection enables us to choose the balance between minimum magnitude threshold and a number of false alarms. Workplace Geophysical Institute Contact Hana Krejzlíková, kniha@ig.cas.cz, Tel.: 267 103 028 Year of Publishing 2020 Electronic address https://academic.oup.com/gji/article-abstract/219/1/672/5532359?redirectedFrom=fulltext
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