{"title":"具有表面声速约束的声速剖面构造方法","authors":"Meiqin Liu;Taoyong Jin;Jianhu Zhao;Meng Wu","doi":"10.1109/TGRS.2025.3570970","DOIUrl":null,"url":null,"abstract":"There are common sound speed errors resulting from insufficient density of sound speed profile (SSP) stations in a multibeam survey. The existing methods typically use empirical orthogonal function (EOF) analysis to create new SSPs that can mitigate sounding errors coursed by sound speed errors, but they are often inefficient due to their iterative nature. To address this issue, we propose a method for constructing SSPs with surface sound speeds (SSSs) constraints to reduce the sounding errors. This method begins by standardizing the measured SSPs through unified stratification, followed by the clustering of the standardized SSPs (SSSPs). Next, the clustered SSSPs, along with highly accurate SSSs from the transducer, are utilized to estimate the parameters for the inverse distance weighting (IDW)-based spatiotemporal interpolation formula. Finally, the determined formula is employed in the depth direction to construct an interpolated SSP (ISSP), which is then used to correct sounding errors. Experiments verified the proposed method and the results show that the root mean squares (RMSs) of the sounding errors improved by approximately 83%, decreasing from 1.169 m of the alternative SSP (ASSP) to 0.202 m of the ISSP. Additionally, the computational time of the ISSP was reduced by a factor of 67 compared to the EOF method.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-11"},"PeriodicalIF":8.6000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Sound Speed Profile Construction Method With Surface Sound Speed Constraints\",\"authors\":\"Meiqin Liu;Taoyong Jin;Jianhu Zhao;Meng Wu\",\"doi\":\"10.1109/TGRS.2025.3570970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are common sound speed errors resulting from insufficient density of sound speed profile (SSP) stations in a multibeam survey. The existing methods typically use empirical orthogonal function (EOF) analysis to create new SSPs that can mitigate sounding errors coursed by sound speed errors, but they are often inefficient due to their iterative nature. To address this issue, we propose a method for constructing SSPs with surface sound speeds (SSSs) constraints to reduce the sounding errors. This method begins by standardizing the measured SSPs through unified stratification, followed by the clustering of the standardized SSPs (SSSPs). Next, the clustered SSSPs, along with highly accurate SSSs from the transducer, are utilized to estimate the parameters for the inverse distance weighting (IDW)-based spatiotemporal interpolation formula. Finally, the determined formula is employed in the depth direction to construct an interpolated SSP (ISSP), which is then used to correct sounding errors. Experiments verified the proposed method and the results show that the root mean squares (RMSs) of the sounding errors improved by approximately 83%, decreasing from 1.169 m of the alternative SSP (ASSP) to 0.202 m of the ISSP. Additionally, the computational time of the ISSP was reduced by a factor of 67 compared to the EOF method.\",\"PeriodicalId\":13213,\"journal\":{\"name\":\"IEEE Transactions on Geoscience and Remote Sensing\",\"volume\":\"63 \",\"pages\":\"1-11\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Geoscience and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11006130/\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Geoscience and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11006130/","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Sound Speed Profile Construction Method With Surface Sound Speed Constraints
There are common sound speed errors resulting from insufficient density of sound speed profile (SSP) stations in a multibeam survey. The existing methods typically use empirical orthogonal function (EOF) analysis to create new SSPs that can mitigate sounding errors coursed by sound speed errors, but they are often inefficient due to their iterative nature. To address this issue, we propose a method for constructing SSPs with surface sound speeds (SSSs) constraints to reduce the sounding errors. This method begins by standardizing the measured SSPs through unified stratification, followed by the clustering of the standardized SSPs (SSSPs). Next, the clustered SSSPs, along with highly accurate SSSs from the transducer, are utilized to estimate the parameters for the inverse distance weighting (IDW)-based spatiotemporal interpolation formula. Finally, the determined formula is employed in the depth direction to construct an interpolated SSP (ISSP), which is then used to correct sounding errors. Experiments verified the proposed method and the results show that the root mean squares (RMSs) of the sounding errors improved by approximately 83%, decreasing from 1.169 m of the alternative SSP (ASSP) to 0.202 m of the ISSP. Additionally, the computational time of the ISSP was reduced by a factor of 67 compared to the EOF method.
期刊介绍:
IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.