初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域微地震数据映射到Shearlet域,利用AIC(Akaike Information Criterion)模型对Shearle...初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域微地震数据映射到Shearlet域,利用AIC(Akaike Information Criterion)模型对Shearlet域各尺度层的数据实现初步识别,最小AIC值作为初至时刻。通过大量实验验证Shearlet-AIC算法在低至-13 d B信噪比下自动拾取的准确性,证实该算法优于传统初至拾取算法,解决了传统初至拾取算法在低信噪比时难以有效拾取微地震初至的难题。展开更多
在采用高斯径向基函数的相关向量机(RVM)回归模型中,核参数与模型性能之间关系复杂,针对如何确定RVM核参数的问题,提出一种基于AIC准则选择RVM的核参数的方法。首先基于Akaike Information Criterion(AIC)思想,得出一种新的统计量Q,同时...在采用高斯径向基函数的相关向量机(RVM)回归模型中,核参数与模型性能之间关系复杂,针对如何确定RVM核参数的问题,提出一种基于AIC准则选择RVM的核参数的方法。首先基于Akaike Information Criterion(AIC)思想,得出一种新的统计量Q,同时将Q作为适应度函数;然后利用微分进化算法(Differential Evolution Algorithm,DE)对核参数进行寻优,以此选择确定核参数;最后利用该算法建立RVM回归模型对黄金价格进行短期预测。实验结果表明,该模型较传统方法建立的预测模型具有更高的拟合精度和更好的泛化能力,进一步证明基于AIC准则选择RVM的核参数的方法的可行性和有效性。展开更多
In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likeliho...In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.展开更多
文摘初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域微地震数据映射到Shearlet域,利用AIC(Akaike Information Criterion)模型对Shearlet域各尺度层的数据实现初步识别,最小AIC值作为初至时刻。通过大量实验验证Shearlet-AIC算法在低至-13 d B信噪比下自动拾取的准确性,证实该算法优于传统初至拾取算法,解决了传统初至拾取算法在低信噪比时难以有效拾取微地震初至的难题。
文摘在采用高斯径向基函数的相关向量机(RVM)回归模型中,核参数与模型性能之间关系复杂,针对如何确定RVM核参数的问题,提出一种基于AIC准则选择RVM的核参数的方法。首先基于Akaike Information Criterion(AIC)思想,得出一种新的统计量Q,同时将Q作为适应度函数;然后利用微分进化算法(Differential Evolution Algorithm,DE)对核参数进行寻优,以此选择确定核参数;最后利用该算法建立RVM回归模型对黄金价格进行短期预测。实验结果表明,该模型较传统方法建立的预测模型具有更高的拟合精度和更好的泛化能力,进一步证明基于AIC准则选择RVM的核参数的方法的可行性和有效性。
文摘In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.
