本着提高安徽省地面气温资料质量的宗旨,文中采用模糊C均值(Fuzzy C-Means,FCM)聚类法进行地面气温资料的质量控制研究。具体执行过程中,通过FCM将区域内各测温划分为若干气温相似的聚类,定义离群率(空间尺度)和离群速度(时间尺度)识别...本着提高安徽省地面气温资料质量的宗旨,文中采用模糊C均值(Fuzzy C-Means,FCM)聚类法进行地面气温资料的质量控制研究。具体执行过程中,通过FCM将区域内各测温划分为若干气温相似的聚类,定义离群率(空间尺度)和离群速度(时间尺度)识别出气温资料中的离群值。进一步基于专家场模型(Fields of Experts,FoEs)对识别出的气温离群值进行订正,FoEs考虑了邻近站和本站前后时次的气温信息。与传统方法相比,文中算法从整体气温出发,不需要设置气温参考值,FoEs不仅能够订正离群资料,还能对连续缺测资料进行插补。因而文中的方法具有实用性和科学性,较适合计算大样本的气温数据集。展开更多
To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the freque...To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.展开更多
文摘本着提高安徽省地面气温资料质量的宗旨,文中采用模糊C均值(Fuzzy C-Means,FCM)聚类法进行地面气温资料的质量控制研究。具体执行过程中,通过FCM将区域内各测温划分为若干气温相似的聚类,定义离群率(空间尺度)和离群速度(时间尺度)识别出气温资料中的离群值。进一步基于专家场模型(Fields of Experts,FoEs)对识别出的气温离群值进行订正,FoEs考虑了邻近站和本站前后时次的气温信息。与传统方法相比,文中算法从整体气温出发,不需要设置气温参考值,FoEs不仅能够订正离群资料,还能对连续缺测资料进行插补。因而文中的方法具有实用性和科学性,较适合计算大样本的气温数据集。
基金supported by the National Natural Science Foundation of China(No.NSFC 41204101)Open Projects Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(No.PLN201733)+1 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2015051)Open Projects Fund of the Natural Gas and Geology Key Laboratory of Sichuan Province(No.2015trqdz03)
文摘To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.
