Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning

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dc.contributor.authorLara, Pablo
dc.contributor.authorBletery, Quentin
dc.contributor.authorAmpuero, Jean-Paul
dc.contributor.authorInza Callupe, Lamberto Adolfo
dc.contributor.authorTavera, Hernando
dc.date.accessioned2023-11-20T20:57:38Z
dc.date.available2023-11-20T20:57:38Z
dc.date.issued2023-11-02
dc.description.abstractWe introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning (ML) algorithms designed to detect, locate, and estimate the magnitude of an earthquake starting from 3 s of P-waves recorded by a single station. The system is made of six Ensemble ML algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral, and cepstral domains. The training set comprises data sets from Peru, Chile, Japan, and the STEAD global data set. E3WS consists of three sequential stages: detection, P-phase picking, and source characterization. The latter involves magnitude, epicentral distance, depth, and back azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis-classified as earthquakes) and very few false negatives (earthquakes mis-classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P-phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single-station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time-dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.es_ES
dc.description.peer-reviewPor pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.citationLara, P., Bletery, Q., Ampuero, J. P., Inza, A., & Tavera, H. (2023). Earthquake Early Warning starting from 3 s of records on a single station with machine learning.==$Journal of Geophysical Research: Solid Earth, 128$==(11), e2023JB026575. https://doi.org/10.1029/2023JB026575es_ES
dc.identifier.doihttps://doi.org/10.1029/2023JB026575es_ES
dc.identifier.govdocindex-oti2018
dc.identifier.journalJournal of Geophysical Research: Solid Earthes_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12816/5488
dc.language.isoenges_ES
dc.publisherAmerican Geophysical Uniones_ES
dc.relation.ispartofurn:issn:2169-9356
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_ES
dc.subjectEarthquake Early Warning Systemes_ES
dc.subjectSingle stationes_ES
dc.subjectMachine Learninges_ES
dc.subjectFeature extractiones_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.00es_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.04es_ES
dc.titleEarthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES

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