Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques

This study aims to classify seafloor sediments using physics‐inspired and data‐driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S‐ and P‐wave velocities and depth. The soil information reported in...

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Bibliographic Details

Published in:	Computer-aided civil and infrastructure engineering Vol. 39; no. 14; pp. 2105 - 2121
Main Authors:	Park, Junghee, Lee, Jong‐Sub, Yoon, Hyung‐Koo
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc 01.07.2024
Subjects:	Accuracy Algorithms Classification (sedimentation) Effectiveness Machine learning Ocean floor Oversampling Performance enhancement S waves Sediments Soil porosity Wave velocity
ISSN:	1093-9687, 1467-8667
Online Access:	Get full text
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