Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/154744
DC Field | Value | |
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dc.title | Autocorrelation-based Geophysical Bedrock Mapping Using Ambient Noise | |
dc.contributor.author | Zhang, Yunhuo | |
dc.contributor.author | Li, Yunyue Elita | |
dc.contributor.author | Ku Taeseo | |
dc.date.accessioned | 2019-05-27T02:41:25Z | |
dc.date.available | 2019-05-27T02:41:25Z | |
dc.date.issued | 2018-11-22 | |
dc.identifier.citation | Zhang, Yunhuo, Li, Yunyue Elita, Ku Taeseo (2018-11-22). Autocorrelation-based Geophysical Bedrock Mapping Using Ambient Noise. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/154744 | |
dc.description.abstract | Bedrock detection is an essential and critical task in geotechnical site investigation for mega infrastructure and underground construction projects. This paper presents a new approach of geophysical near-surface underground survey, namely autocorrelation seismic interferometry which is using ambient noise and conducted in passive manner. A preliminary field testing carried out in Singapore is presented to demonstrate the concept and results in steps. A by-product of realistic average Vp of the soil layers above bedrock can be estimated reasonably by the proposed method. In view of the proof of concept of detecting bedrock accurately, and the easy and flexible field implementation, also less subject to site conditions, the proposed approach can be an attractive option for geotechnical site investigation especially for delineating the bedrock topography. | |
dc.source | Elements | |
dc.subject | Passive seismic mapping | |
dc.subject | ambient noise | |
dc.subject | autocorrelation | |
dc.subject | bedrock detection | |
dc.type | Conference Paper | |
dc.date.updated | 2019-05-24T09:09:07Z | |
dc.contributor.department | CIVIL AND ENVIRONMENTAL ENGINEERING | |
dc.published.state | Published | |
Appears in Collections: | Staff Publications Elements |
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File | Description | Size | Format | Access Settings | Version | |
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31 KKHTCNN - Zhang et al 2018.pdf | 343.15 kB | Adobe PDF | OPEN | None | View/Download |
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