Bathymetric Prediction from Multi-source Satellite Altimetry Gravity Data

doi:10.11947/j.JGGS.2019.0106

Journal of Geodesy and Geoinformation Science ›› 2019, Vol. 2 ›› Issue (1): 49-58.doi: 10.11947/j.JGGS.2019.0106

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Bathymetric Prediction from Multi-source Satellite Altimetry Gravity Data

Diao FAN¹,Shanshan LI¹(),Shuyu MENG²,Chi ZHANG¹,Jinkai FENG¹,Yan HUANG¹,Jiawei DU¹,Zhibin XING¹

^1. Information Engineering University, Zhengzhou 450001, China
^2. Xi’an Aerors Data Technology Co.Ltd, Xi’an 710054, China

Received:2018-11-11 Accepted:2019-01-09 Online:2019-03-20 Published:2020-03-20
Contact: Shanshan LI E-mail:zzy_lily@sina.com
About author:Diao FAN (1991—), male, PhD candidate, majors in physical geodesy and spatial marine surveying and mapping engineering.E-mail: fandiao2311@mails.jlu.edu.cn
Supported by:
The National Natural Science Foundation of China(41774021);The National Natural Science Foundation of China(41404020);The National Natural Science Foundation of China(41774018);The National Natural Science Foundation of China(41674082);The National Natural Science Foundation of China(41504018);The National Natural Science Foundation of China(41674026);The State Key Laboratory of Geo-Information Engineering(SKLGIE2016-M-3-2);The School Project(2017503902);The School Project(2018222)

Abstract

Abstract:

According to the “theoretical admittance " and the "observation admittance" of the actual data, the theoretical value of effective elastic thickness in the study area was 10km. Combining the gravity anomalies and vertical gravity gradient anomalies, the admittance function is used to construct the 1'×1' bathymetry model over the Philippine Sea by using the adaptive weighting technique. It is found that the accuracy of the bathymetry model constructed is the highest when the ratio of inversion result of vertical gravity gradient anomalies and inversion result of gravity anomalies is 2∶3. At the same time, using multi-source gravity data to predict bathymetry could synthesize the superiority of gravity anomalies and vertical gravity gradient anomalies on the different seafloor topography, and the accuracy is better than bathymetry model that only used gravity anomalies or vertical gravity gradient anomalies. Taking the ship test data as the checking condition, the accuracy of predicting model is slightly lower than that of V18.1 model and improved by 27.17% and 39.02% respectively compared with the ETOPO1 model and the DTU10 model. Check points which the absolute value of the relative error of the predicting model is in the range of 5% accounted for 94.25% of the total.

Key words: bathymetry; admittance function; isostatic compensation; effective elastic thickness; gravity data

Diao FAN,Shanshan LI,Shuyu MENG,Chi ZHANG,Jinkai FENG,Yan HUANG,Jiawei DU,Zhibin XING. Bathymetric Prediction from Multi-source Satellite Altimetry Gravity Data[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(1): 49-58.

Figures/Tables 12

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References 29

[1]	SANDWELL D T, SMITH W H F, GILLE S , et al. Bathymetry from Space: Rationale and Requirements for A New, High-resolution Altimetric Mission[J]. Comptes Rendus Géoscience, 2006,338(14-15):1049-1062.
[2]	BORN G H, DUNNE J A, LAME D B . Seasat Mission Overview[J]. Science, 1979,204(4400):1405-1406.
[3]	CHENEY R, DOUGLAS B, AGREEN R , et al. The GEOSAT Altimeter Mission: A Milestone in Satellite Oceanography[J]. Eos, Transactions, American Geophysical Union, 1986,67(48):1354-1355.
[4]	GOTTSCHALK D . ERS-1 Mission and System Overview[J]. Die Geowissenschaften, 1991,9:100-101.
[5]	FRANCIS C R, GRAF G, EDWARDS P G , et al. The ERS-2 Spacecraft and Its Payload[J]. ESA Bulletin, 1995(83):13-31.
[6]	FU L L, CHRISTENSEN E J, YAMARONE JR C A , et al. TOPEX/Poseidon Mission Overview[J]. Journal of Geophysical Research: Atmospheres, 1994,99(C12):24369-24381.
[7]	BAUDRY N, DIAMENT M, ALBOUY Y . Precise Location of Unsurveyed Seamounts in the Austral Archipelago Area Using SEASAT Data[J]. Geophysical Journal International, 1987,89(3):869-888.
[8]	SANDWELL D T, GILLE S T, ORCUTT J , et al. Bathymetry from Space is Now Possible[J]. Eos, Transactions, American Geophysical Union, 2003,84(5):37-44.
[9]	LYONS S N, SANDWELL D T, SMITH W H F . Three-Dimensional Estimation of Elastic Thickness Under the Louisville Ridge[J]. Journal of Geophysical Research: Solid Earth, 2000,105(B6):13239-13252.
[10]	PARKER R L . The Rapid Calculation of Potential Anomalies[J]. Geophysical Journal International, 1973,31(4):447-455.
[11]	WATTS A B . An Analysis of Isostasy in the World’s Oceans 1. Hawaiian-Emperor Seamount Chain[J]. Journal of Geophysical Research: Solid Earth, 1978,83(B12):5989-6004.
[12]	HWANG C . A Bathymetric Model for the South China Sea from Satellite Altimetry and Depth Data[J]. Marine Geodesy, 1999,22(1):37-51.
[13]	VERGOS G S, SIDERIS M G . Improving the Estimation of Bottom Ocean Topography with Altimetry Derived Gravity Data Using the Integrated Inverse Method[M] //?D?M J, SCHWARZ K P. Vistas for Geodesy in the New Millennium. Berlin, Heidelberg: Springer, 2002: 529-534.
[14]	LUO Jia, LI Jiancheng, JIANG Weiping , et al. Bathymetry Predication of the South China Sea From Satellite Altimeter Data[J]. Hydrographic Surveying and Charting, 2002,22(1):8-10.
[15]	LUO Jia, LI Jiancheng, JIANG Weiping . Bathymetry Prediction of South China Sea from Satellite Data[J]. Geomatics and Information Science of Wuhan University, 2002,27(3):256-260.
[16]	HU Minzhang, LI Jiancheng, XING Lelin . Global Bathymetry Model Predicted From Vertical Gravity Gradient Anomalies[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(6):558-565, 574. DOI: 10.13485/j.cnki.11-2089.2014.0090.
[17]	OUYANG Mingda, SUN Zhongmiao, ZHAI Zhenhe . Predicting Bathymetry in South China Sea using the Gravity-Geologic Method[J]. Chinese Journal of Geophysics, 2014,57(9):2756-2765.
[18]	OUYANG Mingda, SUN Zhongmiao, ZHAI Zhenhe , et al. Bathymetry Prediction Based on the Admittance Theory of Gravity Anomalies[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(10):1092-1099. DOI: 10.11947/j.AGCS.2015.20140427.
[19]	FAN Diao, LI Shanshan, MENG Shuyu , et al. Predicting Submarine Topography By Linear Regression Analysis[J]. Journal of Chinese Inertial Technology, 2018,26(1):24-32.
[20]	WAN Xiaoyun, RAN Jiangjun, JIN Shuanggen . Sensitivity Analysis of Gravity Anomalies and Vertical Gravity Gradient Data for Bathymetry Inversion[J]. Marine Geophysical Research, 2018: 1-10.
[21]	HU Minzhang, LI Jiancheng, JIN Taoyong , et al. Recovery of Bathymetry over China Sea and Its Adjacent Areas by Combination of Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2015,40(9):1266-1273.
[22]	SMITH W H F, SANDWELL D T . Bathymetric Prediction from Dense Satellite Altimetry and Sparse Shipboard Bathymetry[J]. Journal of Geophysical Research: Solid Earth, 1994,99(B11):21803-21824.
[23]	FU Yongtao, FAN Shouzhi, SHI Xiaobin . Geological Interpretation of the Lithosphere Effective Elastic Thickness[J]. Chinese Journal of Geology, 2005,40(4):585-593.
[24]	LEWIS B T R, DORMAN L M . Experimental Isostasy: 2. An Isostatic Model for the U.S.A. Derived from Gravity and Topographic Data[J]. Journal of Geophysical Research, 1970,75(17):3367-3386.
[25]	SU Daquan . A Study of the Effective Elastic Thickness of the Oceanic Lithosphere[J]. Chinese Journal of Geophysics, 2012,55(10):3259-3265.
[26]	FOSTER M R . The Coefficient of Coherence; Its Estimation and Use in Geophysical Data Processing[J]. Geophysics, 1967,32(4):602-616.
[27]	LUIS J F, NEVES M C . The Isostatic Compensation of the Azores Plateau: A 3D Admittance and Coherence Analysis[J]. Journal of Volcanology and Geothermal Research, 2006,156(1-2):10-22.
[28]	SANDWELL D, GARCIA E, SOOFI K , et al. Toward 1-mGal Accuracy in Global Marine Gravity from CryoSat-2, Envisat, and Jason-1[J]. The Leading Edge, 2013,32(8):892-899.
[29]	WANG Jiapei, SHEN Chongyang, XUAN Songbai . Gravity Checking on the CRUST1.0 Model in the Southeastern Tibetan Plateau[J]. Journal of Geodesy and Geodynamics, 2015,35(4):621-626.

Model	\|Maximum\|	\|Minimum\|	Mean	RMS	Correlation coefficient
Inversion model	611.19	0.10	15.27	111.72	0.9863
Model 1	633.74	0.06	17.83	140.42	0.9777
Model 2	1129.30	0.01	11.47	172.89	0.9737

Model	\|Maximum\|	\|Minimum\|	Mean	RMS	Correlation coefficient
Inversion model	611.19	0.10	15.27	111.72	0.9863
S&S V18.1 model	500.85	0.00	-23.11	96.18	0.9893
ETOPO1 model	1070.60	0.02	-36.26	153.40	0.9724
DTU10 model	1785.90	0.00	-53.41	183.22	0.9597

Model	Maximum	Minimum	Mean	STD
Inversion model	10.80	-22.67	-0.42	2.45
S&S V18.1 model	11.57	-16.56	0.33	1.95
ETOPO1 model	27.31	-35.03	0.48	3.58
DTU10 model	35.48	-56.25	0.81	3.89

Bathymetric Prediction from Multi-source Satellite Altimetry Gravity Data

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