Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea

Chengcheng Zhu,Jinyun Guo,Jinyao Gao,Jiajia Yuan,Xin Jin,Chengming Li

doi:10.3390/rs13040607

Abstract

Shipborne gravity can be used to refine altimeter-derived gravity whose accuracy is low in shallow waters and areas with complex submarine topography. As altimeter-derived gravity only within a small radius around the shipborne data can be corrected by traditional methods, a new method based on multi-layer perceptron (MLP) neural network is proposed to refine the altimeter-derived gravity. Input variables of MLP include the positional information at observation points and geophysical information (from our own South China Sea gravity anomaly model (SCSGA) V1.0 and bathymetry model ETOPO1) at grid points around observation points. Output variables of MLP are the refined residual gravity anomalies at observation points. Training shipborne data are classified into four cases to train four MLP models, which are used to predict the refined gravity anomaly model SCSGA V1.1. Then all of the training shipborne data are used for training an MLP model to predict the refined gravity anomaly model SCSGA V1.2. Assessed by testing shipborne data, the accuracy of SCSGA V1.2 is 0.14 mGal higher than that of SCSGA V1.0, and similar to that of SCSGA V1.1. Compared with the original gravity anomaly model (SCSGA V1.0), the accuracy of the refined gravity anomaly model (SCSGA V1.2) by MLP is improved by 4.4% in areas where the training data are concentrated, and also improved by 2.2% in other areas. Therefore, the method of MLP can be used to refine the altimeter-derived gravity model by shipborne gravity, overcoming the problem of limited correction radius for traditional methods.

Highlights

Precise ocean gravity models play an important role in geodetic and geophysical fields such as studying the Earth’s shape [1], interpreting lithospheric structure [2,3], exploiting marine resources [4], exploring space and improving aviation [5,6].Abundant altimetric data have been collected since the 1970s and are homogenous over the oceans
M1 and M2, suggesting that submarine topography slopes play a more important role than bathymetry in refining altimeter-derived gravity in areas where submarine topography slopes are large. These results shows that the choice of input variables of multi-layer perceptron (MLP) neural network is appropriate
As submarine topography slopes are greater than 100 m/arcmin along the shipborne track, the gravity anomalies at the shipborne observation points interpolated by SCSGA V1.0 and SCSGA V1.2 are different

Summary

Introduction

Precise ocean gravity models play an important role in geodetic and geophysical fields such as studying the Earth’s shape [1], interpreting lithospheric structure [2,3], exploiting marine resources [4], exploring space and improving aviation [5,6].Abundant altimetric data have been collected since the 1970s and are homogenous over the oceans. Precise ocean gravity models play an important role in geodetic and geophysical fields such as studying the Earth’s shape [1], interpreting lithospheric structure [2,3], exploiting marine resources [4], exploring space and improving aviation [5,6]. Altimetric data play a major role in determining marine gravity models [7,8]. The altimetry waveforms can be contaminated by land and reefs, so the accuracy of altimeter-derived gravity decreases with the increasing proximity to the coastline [10,11]. The standard deviation (STD) of altimeter-measured sea surface heights increases with decreasing water depth [10]. Water depth in coastal areas is shallow, so the precision of altimeter-derived gravity is low in shallow waters

Methods

Results

Discussion

Conclusion