The dispersion characteristics of shallow water can be described by the dispersion curves,which contain substantial ocean parameter information.A fast ocean parameter inversion method based on dispersion curves with a...The dispersion characteristics of shallow water can be described by the dispersion curves,which contain substantial ocean parameter information.A fast ocean parameter inversion method based on dispersion curves with a single hydrophone is presented in this paper.The method is achieved through Bayesian theory.Several sets of dispersion curves extracted from measured data are used as the input function.The inversion is performed by matching a replica calculated with a dispersion formula.The bottom characteristics can be described by the bottom reflection phase shift parameter P.The propagation range and the depth can be inverted quickly when the seabed parameters are represented by on parameter P.The inversion results improve the inversion efficiency of the seabed parameters.Consequently,the inversion efficiency and accuracy are improved while the number of inversion parameters is decreased and the computational speed of replica is increased.The inversion results have lower error than the reference values,and the dispersion curves calculated with inversion parameters are also in good agreement with extracted curves from measured data;thus,the effectiveness of the inversion method is demonstrated.展开更多
In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the w...In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters.展开更多
The tide-induced mixing plays an important role in the regulation of ocean circulation.Numerical simulation of continental shelf circulation is found to exhibit an unreasonable vertical thermohaline structure without ...The tide-induced mixing plays an important role in the regulation of ocean circulation.Numerical simulation of continental shelf circulation is found to exhibit an unreasonable vertical thermohaline structure without consideration of tide effects.In this study,we establish a harmonic analyzed parameterization of tide-induced(HAT) mixing,by which means to derive time-depended function of mixing coefficient based on harmonic analysis of the vertical mixing coefficient.By employing HAT mixing parameterization scheme,a series of numerical experiments are conducted for the Yellow Sea.Numerical results show that an ocean circulation model with the HAT mixing involved is capable of reproducing the reasonable thermohaline structure of the Yellow Sea Cold Water Mass,similar to structures produced by explicit tidal forcing on the open boundary.The advantage of the HAT method is its faster computation time,compared with models that directly resolve explicit tidal motion.The HAT parameterization for the tide-induced mixing has potential to improve both the accuracy and efficiency of ocean circulation and climate models.展开更多
We develop a new approach to estimating bottom parameters based on the Bayesian theory in deep ocean. The solution in a Bayesian inversion is characterized by its posterior probability density (PPD), which combines ...We develop a new approach to estimating bottom parameters based on the Bayesian theory in deep ocean. The solution in a Bayesian inversion is characterized by its posterior probability density (PPD), which combines prior information about the model with information from an observed data set. Bottom parameters are sensitive to the transmission loss (TL) data in shadow zones of deep ocean. In this study, TLs of different frequencies from the South China Sea in the summer of 2014 are used as the observed data sets. The interpretation of the multidimensional PPD requires the calculation of its moments, such as the mean, covariance, and marginal distributions, which provide parameter estimates and uncertainties. Considering that the sensitivities of shallow- zone TLs vary for different frequencies of the bottom parameters in the deep ocean, this research obtains bottom parameters at varying frequencies. Then, the inversion results are compared with the sampling data and the correlations between bottom parameters are determined. Furthermore, we show the inversion results for multi- frequency combined inversion. The inversion results are verified by the experimental TLs and the numerical results, which are calculated using the inverted bottom parameters for different source depths and receiver depths at the corresponding frequency.展开更多
The coupled ice- ocean model for the Bohai Sea is used for simulating the freezing, melting, and variation of ice cover and the heat bal- ance at the sea- ice, air- ice, and air- sea interfaces of the Bohai Sea during...The coupled ice- ocean model for the Bohai Sea is used for simulating the freezing, melting, and variation of ice cover and the heat bal- ance at the sea- ice, air- ice, and air- sea interfaces of the Bohai Sea during the entire winter in 1998 ̄1999 and 2000 ̄2001. The cou- pled model is forced by real time numerical weather prediction fields. The results show that the thermodynamic effects of atmosphere and ocean are very important for the evolvement of ice in the Bohai Sea, especially in the period of ice freezing and melting. Ocean heat flux plays a key role in the thermodynamic coupling. The simulation also presents the different thermodynamic features in the ice covered region and the marginal ice zone. Ice thickness, heat budget at the interface, and surface sea temperature, etc. between the two representative points are discussed.展开更多
国际Argo(Array for Real-time Geostrophic Oceanography)计划的实施,提供了前所未有的全球深海大洋0~2000m水深范围内的海水温度和盐度观测资料,在大气和海洋科研业务中应用这一全新的资料,是深入认识大气和海洋变异、提高我国气候...国际Argo(Array for Real-time Geostrophic Oceanography)计划的实施,提供了前所未有的全球深海大洋0~2000m水深范围内的海水温度和盐度观测资料,在大气和海洋科研业务中应用这一全新的资料,是深入认识大气和海洋变异、提高我国气候预测、海洋监测分析和预报能力的一个关键所在。通过开发非线性温-盐协调同化方案和利用同化高度计资料来调整模式的温度和盐度场,建立了可同化包括Argo等多种海洋观测资料的全球海洋资料变分同化系统,提高了对全球海洋的监测分析能力。实现了海洋资料同化系统与全球海气耦合模式的耦合,显著提高了短期气候预测水平。利用Argo资料改进了海洋动力模式中的物理过程参数化方案,有效提高了海洋模式对真实大洋的模拟能力和对厄尔尼诺/拉尼娜的预测能力。开发了利用Argo浮标漂流轨迹推算全球海洋表层和中层流的方法,提高了推算的全球表层流、中层流资料质量,有效弥补了洋流观测的匮乏。展开更多
Inversion of seawater physical parameters (temperature, salinity and density) from seismic data is an important part of Seismic Oceanography, which was raised recent years to study physical oceanography. However prese...Inversion of seawater physical parameters (temperature, salinity and density) from seismic data is an important part of Seismic Oceanography, which was raised recent years to study physical oceanography. However present methods have problems that inversion accuracy is not high or inverted parameters are incomprehensive. To overcome these problems, this paper derives Allied Elastic Impedance (AEI), from which we can extract acoustic velocity and density of seawater directly. Furthermore this paper proposes a method to fit temperature and salinity with acoustic velocity and density respectively, breaking through the limitation that temperature and salinity can only be extracted from acoustic velocity. After applying it to model and real data, we find that this method not only solves the problem that ocean density is hard to extract, but also increases accuracy of other parameters, with the temperature and salinity resolution of 0.06°C and 0.02 psu respectively. All results show that AEI is promising in inversion of seawater physical parameters.展开更多
基金The Scientific Research Foundation of Jiangsu University of Science and Technology for Recruited Talents under contract No.1032931907the Basic Science (Natural Science) General Program of Jiangsu Province Higher Education Institutions under contract No.21KJD140001。
文摘The dispersion characteristics of shallow water can be described by the dispersion curves,which contain substantial ocean parameter information.A fast ocean parameter inversion method based on dispersion curves with a single hydrophone is presented in this paper.The method is achieved through Bayesian theory.Several sets of dispersion curves extracted from measured data are used as the input function.The inversion is performed by matching a replica calculated with a dispersion formula.The bottom characteristics can be described by the bottom reflection phase shift parameter P.The propagation range and the depth can be inverted quickly when the seabed parameters are represented by on parameter P.The inversion results improve the inversion efficiency of the seabed parameters.Consequently,the inversion efficiency and accuracy are improved while the number of inversion parameters is decreased and the computational speed of replica is increased.The inversion results have lower error than the reference values,and the dispersion curves calculated with inversion parameters are also in good agreement with extracted curves from measured data;thus,the effectiveness of the inversion method is demonstrated.
基金This research is jointly funded by the National Key Research and Development Program of China(No.2017 YFC0307401)the National Natural Science Foundation of China(No.41230318)+1 种基金the Fundamental Research Funds for the Central Universities(No.201964017)and the National Science and Technology Major Project of China(No.2016ZX05024-001-002).
文摘In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters.
基金The National Key Research and Development Program of China under contract No.2017YFC1404201the National Natural Science Foundation of China(NSFC)under contract Nos 41606040 and 41606036+1 种基金the NSFC-Shandong Joint Fund for Marine Science Research Centers under contract No.U1606405the National High Technology Research and Development Program(863 Program)of China under contract No.2013AA09A506
文摘The tide-induced mixing plays an important role in the regulation of ocean circulation.Numerical simulation of continental shelf circulation is found to exhibit an unreasonable vertical thermohaline structure without consideration of tide effects.In this study,we establish a harmonic analyzed parameterization of tide-induced(HAT) mixing,by which means to derive time-depended function of mixing coefficient based on harmonic analysis of the vertical mixing coefficient.By employing HAT mixing parameterization scheme,a series of numerical experiments are conducted for the Yellow Sea.Numerical results show that an ocean circulation model with the HAT mixing involved is capable of reproducing the reasonable thermohaline structure of the Yellow Sea Cold Water Mass,similar to structures produced by explicit tidal forcing on the open boundary.The advantage of the HAT method is its faster computation time,compared with models that directly resolve explicit tidal motion.The HAT parameterization for the tide-induced mixing has potential to improve both the accuracy and efficiency of ocean circulation and climate models.
基金Supported by the National Natural Science Foundation of China under Grant No 11174235
文摘We develop a new approach to estimating bottom parameters based on the Bayesian theory in deep ocean. The solution in a Bayesian inversion is characterized by its posterior probability density (PPD), which combines prior information about the model with information from an observed data set. Bottom parameters are sensitive to the transmission loss (TL) data in shadow zones of deep ocean. In this study, TLs of different frequencies from the South China Sea in the summer of 2014 are used as the observed data sets. The interpretation of the multidimensional PPD requires the calculation of its moments, such as the mean, covariance, and marginal distributions, which provide parameter estimates and uncertainties. Considering that the sensitivities of shallow- zone TLs vary for different frequencies of the bottom parameters in the deep ocean, this research obtains bottom parameters at varying frequencies. Then, the inversion results are compared with the sampling data and the correlations between bottom parameters are determined. Furthermore, we show the inversion results for multi- frequency combined inversion. The inversion results are verified by the experimental TLs and the numerical results, which are calculated using the inverted bottom parameters for different source depths and receiver depths at the corresponding frequency.
基金supported by the National Natural Science Foundation of China under contract Nos 40233032 and 40376006the National High Technolo-gy Research and Development Program of China(“863")under contract Nos 2002AA639340 and 2001 AA631070the Principal Project under contract Nos 2001DIA50040 and 2001CB7l1006.
文摘The coupled ice- ocean model for the Bohai Sea is used for simulating the freezing, melting, and variation of ice cover and the heat bal- ance at the sea- ice, air- ice, and air- sea interfaces of the Bohai Sea during the entire winter in 1998 ̄1999 and 2000 ̄2001. The cou- pled model is forced by real time numerical weather prediction fields. The results show that the thermodynamic effects of atmosphere and ocean are very important for the evolvement of ice in the Bohai Sea, especially in the period of ice freezing and melting. Ocean heat flux plays a key role in the thermodynamic coupling. The simulation also presents the different thermodynamic features in the ice covered region and the marginal ice zone. Ice thickness, heat budget at the interface, and surface sea temperature, etc. between the two representative points are discussed.
文摘国际Argo(Array for Real-time Geostrophic Oceanography)计划的实施,提供了前所未有的全球深海大洋0~2000m水深范围内的海水温度和盐度观测资料,在大气和海洋科研业务中应用这一全新的资料,是深入认识大气和海洋变异、提高我国气候预测、海洋监测分析和预报能力的一个关键所在。通过开发非线性温-盐协调同化方案和利用同化高度计资料来调整模式的温度和盐度场,建立了可同化包括Argo等多种海洋观测资料的全球海洋资料变分同化系统,提高了对全球海洋的监测分析能力。实现了海洋资料同化系统与全球海气耦合模式的耦合,显著提高了短期气候预测水平。利用Argo资料改进了海洋动力模式中的物理过程参数化方案,有效提高了海洋模式对真实大洋的模拟能力和对厄尔尼诺/拉尼娜的预测能力。开发了利用Argo浮标漂流轨迹推算全球海洋表层和中层流的方法,提高了推算的全球表层流、中层流资料质量,有效弥补了洋流观测的匮乏。
文摘Inversion of seawater physical parameters (temperature, salinity and density) from seismic data is an important part of Seismic Oceanography, which was raised recent years to study physical oceanography. However present methods have problems that inversion accuracy is not high or inverted parameters are incomprehensive. To overcome these problems, this paper derives Allied Elastic Impedance (AEI), from which we can extract acoustic velocity and density of seawater directly. Furthermore this paper proposes a method to fit temperature and salinity with acoustic velocity and density respectively, breaking through the limitation that temperature and salinity can only be extracted from acoustic velocity. After applying it to model and real data, we find that this method not only solves the problem that ocean density is hard to extract, but also increases accuracy of other parameters, with the temperature and salinity resolution of 0.06°C and 0.02 psu respectively. All results show that AEI is promising in inversion of seawater physical parameters.