Abstract：Due to the lacking low frequency information and the low resolution of seismic data for deep or ultra-deep land layers, the accurate interpretation of subsequent seismic data is affected. Model-dri-ven low frequency compensation methods have dependence on strict assumptions and inflexible parameter adjustment. The convolutional neural network (CNN) has limited feature extraction ability for subtle changes and no obvious gradient changes, and the network is easy to fall into local optimum, resulting in low frequency undercompensation or low compensation accuracy. Therefore, a low frequency compensation method for pre-stack seismic data combining improved CNN and double constrained loss function is proposed. On the premise of not increasing the computational complexity of CNN, residual blocks network units that can directly learn residual features between input and output are added to solve gradient disappearance. Additionally, batch normalization is adopted to make the network more sensitive to subtle changes, to improve network training efficiency. Since the gradient changes are not obvious, the network convergence is premature. To address the problem, this paper takes the difference and the correlation between the network output and original seismic record as optimization objectives and establishes the loss function by the weighted sum of the mean square error and Pearson distance to calculate the compensation error under double constraints. Finally, the gradient changes become more evident and ensure that the local optimal can be jumped out during gradient descent, so as to improve the low frequency compensation accuracy. The synthetic data and the low frequency compensation results of the real pre-stack seismic data in X area of western China verify the feasibility and effectiveness of the proposed method. Compared with the low frequency compensation method based on CNN and that based on deconvolution combined with broadband Yu low-pass filter, the proposed method can compensate the low frequency components without destroying the original medium and high frequency information.
戴永寿, 高倩倩, 孙伟峰, 万勇, 吴莎莎. 结合改进CNN和双约束损失函数的叠前地震数据低频补偿方法[J]. 石油地球物理勘探, 2022, 57(6): 1287-1295.
DAI Yongshou, GAO Qianqian, SUN Weifeng, WAN Yong, WU Shasha. Low frequency compensation of pre-stack seismic data based on improved CNN and double constrained loss function. Oil Geophysical Prospecting, 2022, 57(6): 1287-1295.
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