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

doi:10.1109/LGRS.2023.3302992

IOCAS-IR > 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 1,2,3; Liu, Min 4; Liu, Na 1; Guo, Jinyun 5; Lin, Lina 1; Zhang, Yuyuan 1; Du, Xing 1; Xu, Yongsheng2 ; Zhu, Chengcheng 6; Wang, Yongkang 7
	2023
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
卷号	20 页码:5
通讯作者	Liu, Min(unhackor@163.com)
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.
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
DOI	10.1109/LGRS.2023.3302992
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41806214] ; National Natural Science Foundation of China[42106232]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001063563700007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	MODEL
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/181818
专题	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.Univ Chinese Acad Sci, Beijing 100864, Peoples R China 4.91001 Unit, Beijing 100841, Peoples R China 5.Shandong Univ Sci & Technol, Coll Geodesy & Geomatics, Qingdao, Peoples R China 6.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China 7.Natl Marine Environm Monitoring Ctr, Dalian 116023, Peoples R China
第一作者单位	中国科学院海洋研究所
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.