Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network

doi:10.1109/LGRS.2023.3302992

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network
	Yang, Lei 1,2,7; Liu, Min 6; Liu, Na 1; Guo, Jinyun 5; Lin, Lina 1; Zhang, Yuyuan 1; Du, Xing 1; Xu, Yongsheng 2; Zhu, Chengcheng 4; Wang, Yongkang 3
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2023
卷号	20 页码:5
关键词	Gravity Bathymetry Satellites Geology Altimetry Geologic measurements Sea measurements convolutional neural network (CNN) fully connected deep neural network (FC-DNN) gravity gravity-geological method satellite altimetry
ISSN号	1545-598X
DOI	10.1109/LGRS.2023.3302992
通讯作者	Liu, Min(unhackor@163.com)
英文摘要	The topography of the seafloor is highly correlated with the local gravity through intrinsically nonlinear relationships across a particular wavelength band. The purpose of this study is to compare a fully connected deep neural network (FC-DNN) and a convolutional neural network (CNN) with the gravity-geological method (GGM) to determine whether deep learning can provide superior predictions of bathymetry. We include the short-wavelength gravity (SG) and geological models as training parameters, and assess the performance of different models and parameter combinations using various inputs. Compared with the CNN method, the FC-DNN with the SG as an input reduces the standard deviation (STD) of bathymetry differences from 118.6 m to about 73.5 m. The FC-DNN with SG reduces the STD of bathymetry differences by up to 13.3% compared with the conventional GGM. Furthermore, we demonstrate that the addition of geological information alongside the SG does not significantly enhance the accuracy. Power spectral density analysis suggests that the FC-DNN is superior for predicting wavelengths shorter than 6 km.
资助项目	National Natural Science Foundation of China[41806214] ; National Natural Science Foundation of China[42106232]
WOS关键词	MODEL
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001063563700007
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/181817]
专题	海洋研究所_海洋环流与波动重点实验室
通讯作者	Liu, Min
作者单位	1.Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Shandong, Peoples R China 3.Natl Marine Environm Monitoring Ctr, Dalian 116023, Peoples R China 4.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China 5.Shandong Univ Sci & Technol, Coll Geodesy & Geomatics, Qingdao, Peoples R China 6.91001 Unit, Beijing 100841, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Liu, Min,Liu, Na,et al. Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023,20:5.
APA	Yang, Lei.,Liu, Min.,Liu, Na.,Guo, Jinyun.,Lin, Lina.,...&Wang, Yongkang.(2023).Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,20,5.
MLA	Yang, Lei,et al."Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20(2023):5.