A letter to the editor constitutes a short communication addressing a range of topics pertinent to the readership of a journal(Dkhar,2018).This format offers several benefits,such as timeliness,accessibility,innovatio...A letter to the editor constitutes a short communication addressing a range of topics pertinent to the readership of a journal(Dkhar,2018).This format offers several benefits,such as timeliness,accessibility,innovation,and conciseness,thereby serving as an effective means to disseminate cuttingedge scientific ideas.Over the past five years,there has been a considerable increase in the number of letters published,representing the highest growth rate(approximately 20%)observed in the last three decades(Figure 1A).In the field of academic publishing,letters to the editor are typically more concise than typical research papers.展开更多
The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing W...The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing Wang and Prof.Zhong-zhi Fu from Nanjing Hydraulic Research Institute,China,and Prof.Yan-hui Ao,Prof.Ching-sheng Huang,Prof.Guang-qiu Jin,Prof.Bin Xu,Prof.Sai-yu Yuan,Prof.Zeng Zhou,Prof.Bo Chen,and Prof Da-wei Guan from Hohai Uiversity,China,for their great effort and contribution to WSE in the year 2023.展开更多
The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the tr...The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.展开更多
The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functio...The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.展开更多
The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing W...The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing Wang and Prof.Zhong-zhi Fu from Nanjing Hydraulic Research Institute,China,and Prof.Yan-hui Ao,Prof.Ching-sheng Huang,Prof.Guang-qiu Jin,Prof.Bin Xu,Prof.Sai-yu Yuan,Prof.Zeng Zhou,Prof.Bo Chen,and Prof Da-wei Guan from Hohai Uiversity,China,for their great effort and contribution to WSE in the year 2022.展开更多
为解决Navi-Trainer Professional 5000型全任务大型船舶操纵模拟器无法识别S-57数据格式,致使最新的官方电子海图(ENC)数据文件无法直接应用的问题,采用Scene Editor软件进行海图数据转换,克服船舶操纵模拟器电子海图数据间不融合的问...为解决Navi-Trainer Professional 5000型全任务大型船舶操纵模拟器无法识别S-57数据格式,致使最新的官方电子海图(ENC)数据文件无法直接应用的问题,采用Scene Editor软件进行海图数据转换,克服船舶操纵模拟器电子海图数据间不融合的问题。以天津港附近水域海图文件转换为例,验证Scene Editor软件进行海图数据转换具有可行性,说明基于ENC数据文件的三维实景建模可广泛应用于船舶操纵的教学、科研和培训。展开更多
文摘A letter to the editor constitutes a short communication addressing a range of topics pertinent to the readership of a journal(Dkhar,2018).This format offers several benefits,such as timeliness,accessibility,innovation,and conciseness,thereby serving as an effective means to disseminate cuttingedge scientific ideas.Over the past five years,there has been a considerable increase in the number of letters published,representing the highest growth rate(approximately 20%)observed in the last three decades(Figure 1A).In the field of academic publishing,letters to the editor are typically more concise than typical research papers.
文摘The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing Wang and Prof.Zhong-zhi Fu from Nanjing Hydraulic Research Institute,China,and Prof.Yan-hui Ao,Prof.Ching-sheng Huang,Prof.Guang-qiu Jin,Prof.Bin Xu,Prof.Sai-yu Yuan,Prof.Zeng Zhou,Prof.Bo Chen,and Prof Da-wei Guan from Hohai Uiversity,China,for their great effort and contribution to WSE in the year 2023.
基金supported by The National 973 program (No. 2007 CB209505)Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601)RIPED Youth Innovation Foundation (No. 2010-A-26-01)
文摘The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.
基金Supported by the National"863"Project(No.2014AA06A605)
文摘The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.
文摘The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing Wang and Prof.Zhong-zhi Fu from Nanjing Hydraulic Research Institute,China,and Prof.Yan-hui Ao,Prof.Ching-sheng Huang,Prof.Guang-qiu Jin,Prof.Bin Xu,Prof.Sai-yu Yuan,Prof.Zeng Zhou,Prof.Bo Chen,and Prof Da-wei Guan from Hohai Uiversity,China,for their great effort and contribution to WSE in the year 2022.
文摘为解决Navi-Trainer Professional 5000型全任务大型船舶操纵模拟器无法识别S-57数据格式,致使最新的官方电子海图(ENC)数据文件无法直接应用的问题,采用Scene Editor软件进行海图数据转换,克服船舶操纵模拟器电子海图数据间不融合的问题。以天津港附近水域海图文件转换为例,验证Scene Editor软件进行海图数据转换具有可行性,说明基于ENC数据文件的三维实景建模可广泛应用于船舶操纵的教学、科研和培训。
