结果表明在非高斯噪声情况下，通过调节参数可以得到比在高斯噪声情况下更好的信噪比增益。
The results indicate that larger SNR gain can be derived via tuning system parameters in non-Gaussian cases than that in the Gaussian noise.
该算法根据支持向量域数据描述本身的特点，利用非高斯性来测量核空间样本接近球形区域分布的程度，并根据此测量结果来优化核参数。
According to the characteristic of SVDD, the proposed algorithm utilizes the non-Gaussian to measure how kernel samples approximate to a spherical area, and then optimize the kernel parameter.
采用贝叶斯指示条件模拟和序贯高斯模拟方法结合建立储层参数模型，很好地预测了储层非均质性展布。
With the combination of Bayes Indicator Conditional Simulation method and Sequential Gaussin Simulation method, the distribution of reservoir heterogeneity has been predicted.
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