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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Vydané v:	Computer-aided civil and infrastructure engineering Ročník 39; číslo 14; s. 2105 - 2121
Hlavní autori:	Park, Junghee, Lee, Jong‐Sub, Yoon, Hyung‐Koo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Wiley Subscription Services, Inc 01.07.2024
Predmet:	Accuracy Algorithms Classification (sedimentation) Effectiveness Machine learning Ocean floor Oversampling Performance enhancement S waves Sediments Soil porosity Wave velocity
ISSN:	1093-9687, 1467-8667
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