Machine Learning–Based Digital Integration of Geotechnical and Ultrahigh–Frequency Geophysical Data for Offshore Site Characterizations-Reference-Cited by-同舟云学术

Machine Learning–Based Digital Integration of Geotechnical and Ultrahigh–Frequency Geophysical Data for Offshore Site Characterizations

Published:2021-12 Issue:12 Volume:147 Page:
ISSN:1090-0241
Container-title:Journal of Geotechnical and Geoenvironmental Engineering
language:en
Short-container-title:J. Geotech. Geoenviron. Eng.

Author:

Chen Jinbo¹^ORCID,Vissinga Marianne²,Shen Yi³^ORCID,Hu Shuang⁴,Beal Elizabeth⁵,Newlin Jason⁶

Affiliation:

1. Geotechnical Engineer, Shell Global Solutions (US), Inc., 150 N. Dairy Ashford Rd., Houston, TX 77079 (corresponding author). ORCID: .

2. Geophysicist, Shell International Exploration and Production, Inc., 150 N. Dairy Ashford Rd., Houston, TX 77079.

3. Geophysicist, Shell International Exploration and Production, Inc., 150 N. Dairy Ashford Rd., Houston, TX 77079. ORCID: .

4. Geotechnical Engineer, Shell International Exploration and Production, Inc., 150 N. Dairy Ashford Rd., Houston, TX 77079.

5. Principal Geophysicist, Shell International Exploration and Production, Inc., 150 N. Dairy Ashford Rd., Houston, TX 77079.

6. Civil Marine Team Lead, Shell International Exploration and Production, Inc., 150 N. Dairy Ashford Rd., Houston, TX 77079.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Geotechnical Engineering and Engineering Geology,General Environmental Science

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GT.1943-5606.0002702

Reference33 articles.

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3. Beale, M., M. Hagan, and H. Demuth. 2010. Neural network toolbox user’s guide. Natick, MA: MathWorks.

4. Bishop, C. M. 2006. Pattern recognition and machine learning. New York: Springer.

5. AUV technology for seabed characterization and geohazards assessment

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