BP神经网络和SVD算法联合的地震数据去噪方法

传统的地震数据去噪方法,由于过多依赖数据的先验信息而使得去噪效果不佳。为了更有效地压制地震数据噪声,结合BP网络和奇异值分解(SVD)算法的各自特点,提出了联合去噪方法。该方法分别对BP网络的拓扑结构和实验方案的选取进行了深入探讨,最终确定实验方法为:首先将含噪地震数据经过BP网络分离,然后将输出的噪声经过SVD算法重构,得到联合算法输出的噪声,最后将含噪地震数据与输出噪声相减,即可得去噪后数据。叠前和叠后地震数据实验均表明该方法的可行性与有效性。通过与传统去噪算法对比,该方法去噪后的均方误差更低,信噪比更高,表明其对实际地震数据去噪效果更佳。

Joint de noising method of seismic data via BP neural network and SVD algorithm

In the traditional de noising method of the seismic data, it has poor effect because it depends on prior data too much. In order to suppress seismic data noise more effectively, a joint de noising method based on the respective characteristics of BP network and SVD algorithm is proposed. The proposed method has carried on the thorough discussion to the BP network structure and the experimental methods. The determined experimental method is as follows: firstly, the noisy seismic data are separated by BP network, and then the output noise is reconstructed by SVD algorithm, which is the output noise of the joint algorithm. Finally, the de noised seismic data can be obtained by subtracting the noisy seismic data from the output noise. Experiments on pre stack and post stack seismic data demonstrate that the proposed method is feasible and effective. Compared with the traditional de noising algorithm, the MSE (mean square error) of the proposed method is lower and the SNR (signal to noise ratio) is higher, which shows that it has better de noising effect on actual seismic data.