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Article: Toward improved urban earthquake monitoring through deep-learning-based noise suppression

TitleToward improved urban earthquake monitoring through deep-learning-based noise suppression
Authors
Issue Date2022
Citation
Science Advances, 2022, v. 8, n. 15, article no. eabl3564 How to Cite?
AbstractEarthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to ~0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.
Persistent Identifierhttp://hdl.handle.net/10722/324217
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, Lei-
dc.contributor.authorLiu, Xin-
dc.contributor.authorZhu, Weiqiang-
dc.contributor.authorZhao, Liang-
dc.contributor.authorBeroza, Gregory C.-
dc.date.accessioned2023-01-13T03:02:17Z-
dc.date.available2023-01-13T03:02:17Z-
dc.date.issued2022-
dc.identifier.citationScience Advances, 2022, v. 8, n. 15, article no. eabl3564-
dc.identifier.urihttp://hdl.handle.net/10722/324217-
dc.description.abstractEarthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to ~0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.-
dc.languageeng-
dc.relation.ispartofScience Advances-
dc.titleToward improved urban earthquake monitoring through deep-learning-based noise suppression-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1126/sciadv.abl3564-
dc.identifier.pmid35417238-
dc.identifier.scopuseid_2-s2.0-85128312421-
dc.identifier.volume8-
dc.identifier.issue15-
dc.identifier.spagearticle no. eabl3564-
dc.identifier.epagearticle no. eabl3564-
dc.identifier.eissn2375-2548-
dc.identifier.isiWOS:000786201300016-

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