Loading…

Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning

S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (VS30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or VS30. The measur...

Full description

Saved in:
Bibliographic Details
Published in:Soils and foundations 2024-12, Vol.64 (6), p.101525, Article 101525
Main Authors: Hayashi, Koichi, Suzuki, Toru, Inazaki, Tomio, Konishi, Chisato, Suzuki, Haruhiko, Matsuyama, Hisanori
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c183t-a0bb57829163f097f13c36a7c894d6a3e4e8f6be2e0d0b4f7c9c3b1e6697b2e3
container_end_page
container_issue 6
container_start_page 101525
container_title Soils and foundations
container_volume 64
creator Hayashi, Koichi
Suzuki, Toru
Inazaki, Tomio
Konishi, Chisato
Suzuki, Haruhiko
Matsuyama, Hisanori
description S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (VS30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or VS30. The measurement of H/V is easier, compared to active surface wave methods (MASW) or microtremor array measurements (MAM). The inversion of H/V is non-unique and it is impossible to obtain unique Vs profiles. We apply deep learning to estimate the Vs profile from H/V together with other information including site coordinates, deep bedrock depths, and geomorphological classification. The pairs of H/V spectra (input layer) and Vs profiles (output layer) are used as training data. An input layer consists of an observed H/V spectrum, site coordinates, deep bedrock depths, and geomorphological classification, and an output layer is a velocity profile. We applied the method to the South Kanto Plain, Japan. We measured MASW, MAM and H/V at approximately 2300 sites. The pairs of H/V spectrum together with their coordinates, geomorphological classification etc. and Vs profile obtained from the inversion of dispersion curve and H/V, compose the training data. A trained neural network predicts Vs profiles from the observed H/V spectra with other information. Predicted Vs profiles and their VS30 are reasonably consistent with true Vs profiles and their VS30. The results implied that the deep learning could estimate Vs profile from H/V together with other information.
doi_str_mv 10.1016/j.sandf.2024.101525
format article
fullrecord <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_sandf_2024_101525</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0038080624001033</els_id><sourcerecordid>S0038080624001033</sourcerecordid><originalsourceid>FETCH-LOGICAL-c183t-a0bb57829163f097f13c36a7c894d6a3e4e8f6be2e0d0b4f7c9c3b1e6697b2e3</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRb0AiVL4Ajb-AZdxnDrJggWqykOqxILuLcceg6s0jmwrqHw9KWHNakYjnau5h5A7DisOXN4fVkn31q0KKMrzZV2sL8gCQNQMapBX5DqlA4AsgPMFcduU_VFn33_Qd_alR6QjdsH4fKJDDM53mKiL4Ug_Q_Tfoc-6YzmwEWP2Rnc0DWhynJY4hYREW53Q0tBTizjQDnXsp-wbcul0l_D2by7J_mm737yw3dvz6-ZxxwyvRWYa2nZd1UXDpXDQVI4LI6SuTN2UVmqBJdZOtlggWGhLV5nGiJajlE3VFiiWRMyxJoaUIjo1xKldPCkO6mxHHdSvHXW2o2Y7E_UwUzh9NnqMKhmPvUHr41RO2eD_5X8APM5zSA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning</title><source>DOAJ Directory of Open Access Journals</source><creator>Hayashi, Koichi ; Suzuki, Toru ; Inazaki, Tomio ; Konishi, Chisato ; Suzuki, Haruhiko ; Matsuyama, Hisanori</creator><creatorcontrib>Hayashi, Koichi ; Suzuki, Toru ; Inazaki, Tomio ; Konishi, Chisato ; Suzuki, Haruhiko ; Matsuyama, Hisanori</creatorcontrib><description>S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (VS30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or VS30. The measurement of H/V is easier, compared to active surface wave methods (MASW) or microtremor array measurements (MAM). The inversion of H/V is non-unique and it is impossible to obtain unique Vs profiles. We apply deep learning to estimate the Vs profile from H/V together with other information including site coordinates, deep bedrock depths, and geomorphological classification. The pairs of H/V spectra (input layer) and Vs profiles (output layer) are used as training data. An input layer consists of an observed H/V spectrum, site coordinates, deep bedrock depths, and geomorphological classification, and an output layer is a velocity profile. We applied the method to the South Kanto Plain, Japan. We measured MASW, MAM and H/V at approximately 2300 sites. The pairs of H/V spectrum together with their coordinates, geomorphological classification etc. and Vs profile obtained from the inversion of dispersion curve and H/V, compose the training data. A trained neural network predicts Vs profiles from the observed H/V spectra with other information. Predicted Vs profiles and their VS30 are reasonably consistent with true Vs profiles and their VS30. The results implied that the deep learning could estimate Vs profile from H/V together with other information.</description><identifier>ISSN: 0038-0806</identifier><identifier>DOI: 10.1016/j.sandf.2024.101525</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Horizontal-to-vertical spectral ratio ; Inversion ; Japan ; Machine learning ; Microtremor ; S-wave velocity ; Surface wave</subject><ispartof>Soils and foundations, 2024-12, Vol.64 (6), p.101525, Article 101525</ispartof><rights>2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c183t-a0bb57829163f097f13c36a7c894d6a3e4e8f6be2e0d0b4f7c9c3b1e6697b2e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Hayashi, Koichi</creatorcontrib><creatorcontrib>Suzuki, Toru</creatorcontrib><creatorcontrib>Inazaki, Tomio</creatorcontrib><creatorcontrib>Konishi, Chisato</creatorcontrib><creatorcontrib>Suzuki, Haruhiko</creatorcontrib><creatorcontrib>Matsuyama, Hisanori</creatorcontrib><title>Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning</title><title>Soils and foundations</title><description>S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (VS30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or VS30. The measurement of H/V is easier, compared to active surface wave methods (MASW) or microtremor array measurements (MAM). The inversion of H/V is non-unique and it is impossible to obtain unique Vs profiles. We apply deep learning to estimate the Vs profile from H/V together with other information including site coordinates, deep bedrock depths, and geomorphological classification. The pairs of H/V spectra (input layer) and Vs profiles (output layer) are used as training data. An input layer consists of an observed H/V spectrum, site coordinates, deep bedrock depths, and geomorphological classification, and an output layer is a velocity profile. We applied the method to the South Kanto Plain, Japan. We measured MASW, MAM and H/V at approximately 2300 sites. The pairs of H/V spectrum together with their coordinates, geomorphological classification etc. and Vs profile obtained from the inversion of dispersion curve and H/V, compose the training data. A trained neural network predicts Vs profiles from the observed H/V spectra with other information. Predicted Vs profiles and their VS30 are reasonably consistent with true Vs profiles and their VS30. The results implied that the deep learning could estimate Vs profile from H/V together with other information.</description><subject>Horizontal-to-vertical spectral ratio</subject><subject>Inversion</subject><subject>Japan</subject><subject>Machine learning</subject><subject>Microtremor</subject><subject>S-wave velocity</subject><subject>Surface wave</subject><issn>0038-0806</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRb0AiVL4Ajb-AZdxnDrJggWqykOqxILuLcceg6s0jmwrqHw9KWHNakYjnau5h5A7DisOXN4fVkn31q0KKMrzZV2sL8gCQNQMapBX5DqlA4AsgPMFcduU_VFn33_Qd_alR6QjdsH4fKJDDM53mKiL4Ug_Q_Tfoc-6YzmwEWP2Rnc0DWhynJY4hYREW53Q0tBTizjQDnXsp-wbcul0l_D2by7J_mm737yw3dvz6-ZxxwyvRWYa2nZd1UXDpXDQVI4LI6SuTN2UVmqBJdZOtlggWGhLV5nGiJajlE3VFiiWRMyxJoaUIjo1xKldPCkO6mxHHdSvHXW2o2Y7E_UwUzh9NnqMKhmPvUHr41RO2eD_5X8APM5zSA</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Hayashi, Koichi</creator><creator>Suzuki, Toru</creator><creator>Inazaki, Tomio</creator><creator>Konishi, Chisato</creator><creator>Suzuki, Haruhiko</creator><creator>Matsuyama, Hisanori</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>202412</creationdate><title>Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning</title><author>Hayashi, Koichi ; Suzuki, Toru ; Inazaki, Tomio ; Konishi, Chisato ; Suzuki, Haruhiko ; Matsuyama, Hisanori</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c183t-a0bb57829163f097f13c36a7c894d6a3e4e8f6be2e0d0b4f7c9c3b1e6697b2e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Horizontal-to-vertical spectral ratio</topic><topic>Inversion</topic><topic>Japan</topic><topic>Machine learning</topic><topic>Microtremor</topic><topic>S-wave velocity</topic><topic>Surface wave</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hayashi, Koichi</creatorcontrib><creatorcontrib>Suzuki, Toru</creatorcontrib><creatorcontrib>Inazaki, Tomio</creatorcontrib><creatorcontrib>Konishi, Chisato</creatorcontrib><creatorcontrib>Suzuki, Haruhiko</creatorcontrib><creatorcontrib>Matsuyama, Hisanori</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><jtitle>Soils and foundations</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hayashi, Koichi</au><au>Suzuki, Toru</au><au>Inazaki, Tomio</au><au>Konishi, Chisato</au><au>Suzuki, Haruhiko</au><au>Matsuyama, Hisanori</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning</atitle><jtitle>Soils and foundations</jtitle><date>2024-12</date><risdate>2024</risdate><volume>64</volume><issue>6</issue><spage>101525</spage><pages>101525-</pages><artnum>101525</artnum><issn>0038-0806</issn><abstract>S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (VS30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or VS30. The measurement of H/V is easier, compared to active surface wave methods (MASW) or microtremor array measurements (MAM). The inversion of H/V is non-unique and it is impossible to obtain unique Vs profiles. We apply deep learning to estimate the Vs profile from H/V together with other information including site coordinates, deep bedrock depths, and geomorphological classification. The pairs of H/V spectra (input layer) and Vs profiles (output layer) are used as training data. An input layer consists of an observed H/V spectrum, site coordinates, deep bedrock depths, and geomorphological classification, and an output layer is a velocity profile. We applied the method to the South Kanto Plain, Japan. We measured MASW, MAM and H/V at approximately 2300 sites. The pairs of H/V spectrum together with their coordinates, geomorphological classification etc. and Vs profile obtained from the inversion of dispersion curve and H/V, compose the training data. A trained neural network predicts Vs profiles from the observed H/V spectra with other information. Predicted Vs profiles and their VS30 are reasonably consistent with true Vs profiles and their VS30. The results implied that the deep learning could estimate Vs profile from H/V together with other information.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.sandf.2024.101525</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0038-0806
ispartof Soils and foundations, 2024-12, Vol.64 (6), p.101525, Article 101525
issn 0038-0806
language eng
recordid cdi_crossref_primary_10_1016_j_sandf_2024_101525
source DOAJ Directory of Open Access Journals
subjects Horizontal-to-vertical spectral ratio
Inversion
Japan
Machine learning
Microtremor
S-wave velocity
Surface wave
title Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T17%3A29%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estimating%20S-wave%20velocity%20profiles%20from%20horizontal-to-vertical%20spectral%20ratios%20based%20on%20deep%20learning&rft.jtitle=Soils%20and%20foundations&rft.au=Hayashi,%20Koichi&rft.date=2024-12&rft.volume=64&rft.issue=6&rft.spage=101525&rft.pages=101525-&rft.artnum=101525&rft.issn=0038-0806&rft_id=info:doi/10.1016/j.sandf.2024.101525&rft_dat=%3Celsevier_cross%3ES0038080624001033%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c183t-a0bb57829163f097f13c36a7c894d6a3e4e8f6be2e0d0b4f7c9c3b1e6697b2e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true