| 海洋声远距离传播测量数据中潮汐信号预报的计算机模拟 |
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| 引用本文： | 王朔中, Theodore G. BIRDSALL. 海洋声远距离传播测量数据中潮汐信号预报的计算机模拟[J]. 声学学报, 1996, 21(S1): 739-751. DOI: 10.15949/j.cnki.0371-0025.1996.S1.044 |
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| 作者姓名： | 王朔中 Theodore G. BIRDSALL |
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| 摘 要： | 通过长期监测声波在海洋声道中远距离传播的时间,以对全球海水平均温度的变化进行观测时,对于潮汐的影响必须加以修正。本文讨论ATOC项目中潮汐信号预报的计算机模拟。基于潮汐信号的线性模型,根据在一段预定时间内每隔4小时测量一次所得到的数据,利用伪逆矩阵法可对潮汐所引起的传播时间变化进行预报。传播时间的这种变化是正压潮在整个声波传播路径上所产生的总效果。为了得到所要求的预报精度,必须能估计出包括长周期分潮在内的所有分潮,并分辨一些频率非常接近的谐波分潮。为此采样期不应短于18个月。在建立潮汐模型时,分潮频率是唯一需要用到的参数。由于所有天文分潮的频率都能十分精确地知道,而非天文成分对声波传播的影响很小,因此在线性模型中包括尽可能多的分潮即可有效地减小预报误差。量化是预报误差的主要来源之一。通过对多径信号的平均能有效地减小量化误差。
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| 收稿时间： | 1995-02-20 |
Computer simulation of tidal signal prediction in long range ocean acoustic travel-time measurements |
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| WANG Shuozhong, Theodore G. BIRDSALL. Computer simulation of tidal signal prediction in long range ocean acoustic travel-time measurements[J]. ACTA ACUSTICA, 1996, 21(S1): 739-751. DOI: 10.15949/j.cnki.0371-0025.1996.S1.044 |
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| Authors: | WANG Shuozhong Theodore G. BIRDSALL |
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| Abstract: | When using long-range sound travel-time measurements to monitor the ocean temperature changes due to the global warming,tidal effects must be corrected from the data.This paper presents the computer simulation of tidal signal prediction for the ATOC project.On the basis of a linear model of the tidal signal,and using measurements taken within a specified sampling Period and at 4 hour intervals,a pseudo-inverse method is used to predict the travel-time changes attributed to the aggregate effect of barotropic tides along the sound path.In order to obtain an adequate prediction accuracy,a sampling Period of no less than 18 months is recommended,so that all the major tidal constituentS can be estimated,and some closely spaced frequency components are separable.The linear model should include as many constituents as possible to minimize the prediction error.This is feasible because in modeling the tidal signal,the only parameter needed for each constituent is the frequency,and the frequencies of the astronomical components are known to a high precision,while the non-astronomical components are trivial in this application.Quantization is one of the major sources of prediction errors.Quantization errors can be effectively reduced by means of a multipath averaging procedure. |
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