Separating broad-band site response from single-station seismograms

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dc.contributor.authorZhu C
dc.contributor.authorCotton F
dc.contributor.authorKawase H
dc.contributor.authorBradley, Brendon
dc.date.accessioned2023-06-22T02:39:23Z
dc.date.available2023-06-22T02:39:23Z
dc.date.issued2023en
dc.date.updated2023-05-31T03:15:10Z
dc.description.abstractIn this paper, we explore the use of seismicity data on a single-station basis in site response characterization. We train a supervised deep-learning model, SeismAmp, to recognize and separate seismic site response with reference to seismological bedrock (VS  = 3.45 km s−1) in a broad frequency range (0.2–20 Hz) directly from single-station earthquake recordings (features) in Japan. Ground-truth data are homogeneously created using a classical multistation approach—generalized spectral inversion at a total number of 1725 sites. We demonstrate that site response can be reliably separated from single-station seismograms in an end-to-end approach. When SeismAmp is tested at new sites in both Japan (in-domain) and Europe (cross-domain), it achieves the lowest standard deviation among all tested single-station techniques. We also find that horizontal-to-vertical spectral ratio (HVSR) is not the optimal use of single-station recordings. The individual components of each record carry salient information on site response, especially at high frequencies. However, part of the information is lost in HVSR. SeismAmp could lead to improved site-specific earthquake hazard prediction in cases where recordings are available or can be collected at target sites. It is also a convenient tool to remove repeatable site effects from ground motions, which may benefit other applications, for example, improving the retrieval of seismic source parameters. Finally, SeismAmp is trained on data from Japan, future studies could explore transfer learning for practical applications in other regions.en
dc.identifier.citationZhu C, Cotton F, Kawase H, Bradley B (2023). Separating broad-band site response from single-station seismograms. Geophysical Journal International. 234(3). 2053-2065.en
dc.identifier.doihttp://doi.org/10.1093/gji/ggad187
dc.identifier.issn0956-540X
dc.identifier.issn1365-246X
dc.identifier.urihttps://hdl.handle.net/10092/105596
dc.languageen
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.rightsCopyright The Author(s) 2021. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://cr eativecommons.or g/licenses/by/4.0/ ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://hdl.handle.net/10092/17651en
dc.subjectEarthquake ground motionsen
dc.subjectSite effectsen
dc.subjectDeep learningen
dc.subjectMachine learningen
dc.subjectSite responseen
dc.subject.anzsrc0403 Geologyen
dc.subject.anzsrc0404 Geophysicsen
dc.subject.anzsrc0909 Geomatic Engineeringen
dc.subject.anzsrcFields of Research::37 - Earth sciences::3706 - Geophysics::370609 - Seismology and seismic explorationen
dc.subject.anzsrcFields of Research::40 - Engineering::4013 - Geomatic engineering::401302 - Geospatial information systems and geospatial data modellingen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4611 - Machine learning::461103 - Deep learningen
dc.titleSeparating broad-band site response from single-station seismogramsen
dc.typeJournal Articleen
uc.collegeFaculty of Engineering
uc.departmentCivil and Natural Resources Engineering
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