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  vector computer
The FORTRAN software was executed on an IBM 3081 computer with an FPS-164 attached array processor at the Triangle Universities Computing Center and on a CYBER 205 vector computer.
      
Moreover, timing experiments on a Cyber 205 vector computer show that the algorithm presented has good vectorisation properties.
      
The computer code has been specially developed for implementation on a vector computer.
      
The Stockham algorithm is then proposed for the entire computation of the two-dimensional fast Fourier transform on a vector computer.
      
Numerical experiments are carried out on a scalar and a vector computer.
      
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Phase-shift method is applicable to the accurate migration of steep geological structure in layered medium. However,this method has not found its wide application because of big computation and poor lateral velocity variation ability. To speed up the computation,seisrnic data are processed in partitioned way, then the processed seismic data are stored in disk in the same way. If the data processing is performed by vector computer,the vector Fourier transform program is preferred;and fast Fourier transform may...

Phase-shift method is applicable to the accurate migration of steep geological structure in layered medium. However,this method has not found its wide application because of big computation and poor lateral velocity variation ability. To speed up the computation,seisrnic data are processed in partitioned way, then the processed seismic data are stored in disk in the same way. If the data processing is performed by vector computer,the vector Fourier transform program is preferred;and fast Fourier transform may be chosen when the data processing is done by scalar computer. Tine-consuming operations relating to sine, cosine and evolution are finished by consulting mathematical tables. The trial computation proves the method feasible.

相移法适用于层状介质中局部大倾角构造的精确归位,但由于计算工作量大和不适应速度在横向上的变化,故该方法的广泛应用受到了限制。为了提高运算速度,本文采用了数据分块处理,处理后的数据按块方式进行磁盘存取,这就大大地提高了速度。若在向量机上运行时,可选用本机的向量傅氏变换程序;若在标量机上处理时,可选用运算速度较快的傅氏变换;对于费机时的正弦、余弦、开方计算采用了查表方法。实际试算表明,本文介绍的方法是可行的。

In this paper, Support Vector Machines (SVM) method is introduced to solve a two classes classification problem , on which seismic recordings of nuclear explosions must be distinguished from the recordings of natural earthquakes. Using SVM one needn't waste time on feature extraction but resort to the intrinsic feature extraction ability, which makes it more practical. The learning discipline of SVM is to minimize the structural risk instead of empirical risk, hence the better extensibility is guaranteed. The...

In this paper, Support Vector Machines (SVM) method is introduced to solve a two classes classification problem , on which seismic recordings of nuclear explosions must be distinguished from the recordings of natural earthquakes. Using SVM one needn't waste time on feature extraction but resort to the intrinsic feature extraction ability, which makes it more practical. The learning discipline of SVM is to minimize the structural risk instead of empirical risk, hence the better extensibility is guaranteed. The result of practical application indicates that the performance of SVM has superiority over ANN and can overcome the problem of "over fitting" excellently.

针对核爆地震识别问题的特点,提出利用支持向量机(SVM)方法进行核爆地震的自动识别。该方法借助算法的内在能力来实现特征的选择变换,不必像传统方法那样将很大的精力用于特征空间的降维处理。同时,由于该方法建立在结构风险最小化准则上,而不是仅仅使经验风险最小,所以,它具有好的推广能力。实际数据处理结果表明,该方法在小样本情况下性能优于神经网络,可以很好地克服过学习问题。

Traditional learning machine methods like artificial neural networks have the disadvantages of slow training speed, low generalization capability etc. The highlights of statistical learning theory (SLT), the principle and the crucial elements of support vector machine (SVM) were introduced, and the method for flood forecast modeling based on support vector machine was discussed. In case study, the flood forecast model based on SVM exhibited its properties of high generalization capability, fast training, and...

Traditional learning machine methods like artificial neural networks have the disadvantages of slow training speed, low generalization capability etc. The highlights of statistical learning theory (SLT), the principle and the crucial elements of support vector machine (SVM) were introduced, and the method for flood forecast modeling based on support vector machine was discussed. In case study, the flood forecast model based on SVM exhibited its properties of high generalization capability, fast training, and easy modeling.

 用传统的机器学习方法进行洪水预报建模存在泛化能力难以保障,训练速度慢等一些困难。对统计学习理论和支持向量机的基本内容和核心思想进行了简要的介绍,探讨了基于支持向量机的洪水预报模型的建模方法。通过实例中的应用,该模型显示了泛化能力强,训练速度快,便于建模等优点,有良好的应用前景。

 
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