Open AccessShear Wave Speed of Shallow Seafloor Sediments in the Northern South China Sea and Their Correlations With Physical Parameters
Author(s) -
Kan Guangming,
Cao Guolin,
Wang Jingqiang,
Li Guanbao,
Liu Baohua,
Meng Xiangmei,
Meng Qingsheng
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea000950
Subject(s) - porosity , sediment , geology , grain size , bulk density , shear (geology) , seafloor spreading , mineralogy , significant wave height , wave height , soil science , geotechnical engineering , wind wave , geomorphology , geophysics , oceanography , petrology , soil water
In this study, shear wave speed and related physical properties were measured for 21 seafloor sediment cores collected in the northern South China Sea. Results reveal that the shear wave speed in this study area ranges between 15.57 and 75.55 m/s and demonstrates a pattern of regional distribution. The sediment in the continental shelf area shows characteristics of high shear wave speed, high wet bulk density, low porosity, and large grain size. Contrarily, the sediment in the continental slope area exhibits characteristics of low shear wave speed, low wet bulk density, high porosity, and fine particles. Accordingly, the correlation between shear wave speed and physical properties such as porosity, wet bulk density, water content, and average grain size was studied. Prediction formulas of shear wave speed of sediments in this study area containing single and double physical parameters were established via regression analysis. The comparison between single‐parameter and double‐parameter prediction formulas indicates that the determined coefficient of the double‐parameter prediction formulas based on porosity and average grain size is higher than that of single‐parameter prediction formulas. It indicates that the double‐parameter prediction formulas based on porosity and average grain size have better prediction performance. Moreover, the prediction formulas established in this study were further compared with those of other study areas, and their differences were analyzed accordingly.