[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118135-en":3,"doc-seo-118135-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118135,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea","This study improves bathymetry accuracy predicted by the gravity-geologic method (GGM) through an optimal machine learning model selected from multiple regression techniques. Depth and satellite altimetry-derived free-air gravity anomalies (FAGAs) are used to evaluate model performance, and a tuning density contrast is computed from the satellite-derived FAGAs to estimate enhanced bathymetry. Validation against shipborne depth measurements shows bathymetry predicted by Gaussian process regression with GGM and the tuning density contrast reduces RMSE by 67.40%, reaching 82.64 m accuracy. The approach remains effective even when tuning density exceeds 1.67 g/cm³.","Article  \n# Improved Bathymetry Estimation Using Satellite\n\nAltimetry-Derived Gravity Anomalies and Machine Learning inthe East Sea  \nKwang Bae Kim¹D,Jisung Kim 2,*D and Hong Sik Yun³D  \n1 Department of Civil,Architectural and Environmental System Engineering,Sungkyunkwan University,2066 Seobu-ro,Jangan-gu,Suwon 16419,Republic of Korea;kbkim929@skku.edu  \n2 School of Geography,Faculty of Environment,University of Leeds,Woodhouse Lane,Leeds LS29TT,UK3 Department of Interdisciplinary Program in Crisis,Disaster and Risk Management,SungkyunkwanUniversity,2066 Seobu-ro,Jangan-gu,Suwon 16419,Republic of Korea;yoonhs@skku.edu*Correspondence:gyjki@leeds.ac.uk  \nAbstract:This study aims to improve the accuracy of bathymetry predicted by gravity-geologicmethod(GGM)using the optimal machine learning model selected from machine learning techniques.In this study,several machine learning techniques were utilized to determine the optimal model fromthe performance of depth and gravity anomalies.In addition,a tuning density contrast calculatedfrom satellite altimetry-derived free-air gravity anomalies(FAGAs)was applied to estimate enhancedbathymetry.By comparison with shipborne depth,the accuracy of the bathymetry estimated by usingsatellite altimetry-derived FAGAs and machine learning was evaluated.The findings reveal that thebathymetry predicted by the optimal machine learning using the Gaussian process regression and theGGM with a tuning density contrast can enhance the accuracy of 82.64 m,showing animprovementof 67.40%in the RMSE at shipborne depth measurements.Although the tuning density is largerthan 1.67 g/cm³,bathymetry using satellite altimetry-derived FAGAs and machine learning can beeffectively improved with higher accuracy.  \nKeywords:optimal machine learning;gravity anomalies;density contrast;east sea  \ncheck for  \nupdates  \nCitation:Kim,K.B.;Kim,J.;Yun,HS.Improved Bathymetry EstimationUsing Satellite Altimetry-DerivedGravity Anomalies and MachineLearning in the East Sea.J.Mar.Sci.Eng.2024,12,1520.https://doi.org/10.3390/jmse12091520  \n## 1.Introduction\n\nBecause bathymetry is crucial in understanding the Earth's shape,seafloor evolution,marine resource exploration,marine navigation,and marine environment monitoring,relevant research is continuously being conducted to estimate accurate bathymetry astechnology advances.Several satellite radar altimeters have provided accurate sea surfacetopography by measuring the distance between the satellite and the sea surface.  \nAcademic Editor:Chung-yen Kuo  \nThe sea surface topography derived from the distance measurements of the satelliteradar altimeters between the satellite and the sea surface can be recovered to global marinealtimetry-derived gravity anomalies,which have a more uniform and denser coveragethan the ship tracks.The three main satellite altimetry-derived geophysical parameters,such as marine geoid,marine gravity anomalies,and bathymetry,are correlated with theundulations of the crustal density variations under the seafloor [1].Satellite altimetry-derived free-air gravity anomalies(FAGAs)are critical in generating accurate bathymetrymaps by filling large gaps between the bathymetry data on the shipborne tracks using thetopographic effects in the off-tracks in the ocean [2,3].  \nReceived:12 August 2024  \nRevised:29 August 2024  \nAccepted:1 September 2024  \nPublished:2September 2024  \nCopyright:◎ 2024 by the authors.Licensee MDPI,Basel,Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution(CC BY)license (https://creativecommons.org/licenses/by/  \nSeveral studies about bathymetry predictions using the gravity-geologic method(GGM)from the satellite altimetry-derived FAGAs and the density contrast between sea-water and the ocean bedrock were carried out.Roman(1999)[4]adapted GGM to estimatebedrock elevation beneath unconsolidated materials for bathymetric determinations inthe Barents Sea and the waters around G","cbCaiiI23WT3AvFW","https://ap.wps.com/l/cbCaiiI23WT3AvFW","pdf",8605423,1,21,"English","en",105,"# Introduction\n## Satellite altimetry and gravity anomalies\n## Gravity-geologic method (GGM) and prior studies\n## Machine learning for bathymetry estimation","[{\"question\":\"What does the study aim to improve in bathymetry estimation?\",\"answer\":\"The study aims to improve the accuracy of bathymetry predicted by the gravity-geologic method (GGM) by selecting an optimal machine learning model and applying a tuning density contrast derived from satellite altimetry-derived FAGAs.\"},{\"question\":\"How are satellite altimetry-derived gravity anomalies used?\",\"answer\":\"Satellite altimetry-derived free-air gravity anomalies (FAGAs) provide dense, track-filling information that supports generating bathymetry maps and enhancing estimates between shipborne depth tracks.\"},{\"question\":\"What validation result is reported against shipborne depth data?\",\"answer\":\"Comparison with shipborne depth measurements shows the approach using Gaussian process regression with GGM and the tuning density contrast improves accuracy, achieving an RMSE improvement of 67.40% and 82.64 m accuracy.\"}]","Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea | 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does the study aim to improve in bathymetry estimation?","Question",{"text":75,"@type":76},"The study aims to improve the accuracy of bathymetry predicted by the gravity-geologic method (GGM) by selecting an optimal machine learning model and applying a tuning density contrast derived from satellite altimetry-derived FAGAs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are satellite altimetry-derived gravity anomalies used?",{"text":80,"@type":76},"Satellite altimetry-derived free-air gravity anomalies (FAGAs) provide dense, track-filling information that supports generating bathymetry maps and enhancing estimates between shipborne depth tracks.",{"name":82,"@type":73,"acceptedAnswer":83},"What validation result is reported against shipborne depth data?",{"text":84,"@type":76},"Comparison with shipborne depth measurements shows the approach using Gaussian process regression with GGM and the tuning density contrast improves accuracy, achieving an RMSE improvement of 67.40% 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