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
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.
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.
