【摘要】 经验模态分解(EMD)方法是希尔伯特—黄变换的核心部分,可以将地震数据分解为多阶内蕴模态函数(IMF)分量,不同IMF分量具有不同的频率特性,不同的IMF分量对地震相的敏感程度不同,反映不同的地质信息。利用EMD方法结合kohonen神经网络的地震相分析可进行断层识别以及储层预测。文中将EMD方法应用于中国西部的实际地震资料分析,利用重构信号和分解得到的IMF分量进行波形地震相分析。模型试算和实际资料应用结果表明,用感兴趣的IMF分量能够重构信号,重构后的地震信号能够更加清晰地显示断层展布特征、有利储层范围等,提高了地震资料的信噪比和分辨率,对断层展布特征的认识和油气预测具有重要的参考价值。更多还原

【Abstract】 The EMD method is the central part for Hilbert-Huang transform.With the method the seismic data can be decomposited into multi-order IMF components,different IMF component has different frequency characteristics which reflects different geological information.The seismic facies analysis which is based on the EMD method and the Kohonen neural network method can be used in fault identification and reservoir prediction.In this paper the EMD method was used in the field seismic data processing in the western of China,waveform seismic facies analysis was conducted based on the IMF components which were obtained by signal reconstruction and decomposition.The model trial calculation and application results of the field data show that using of the interested IMF component could reconstruct signal,and the reconstructed signals can better display fault distribution characteristics and favorite reservoir range,as well as raise both the signal to noise ratio and the resolution of the seismic data,they have important reference values for understanding the fault distribution characteristics and the reservoir prediction.更多还原