基于多尺度卷积自编码器的地震噪声智能压制方法及应用OA北大核心CSTPCD

To solve the problems of insufficient generalization,lack of objectivity,and scarcity of noise-free data in reality in routine denoising methods,we establish an intelligent approach for noise reduction and signal preservation by using the generalization be-havior of deep learning.According to the principle of utilizing some observed noise-free data,the data set of synthetic seismogram is first derived from forward modeling,followed by the construction of a convolutional auto-encoding network based on InceptionV4 convolutional module and attention mechanism.The network with great power of feature extraction is pre-trained using synthetic data to tentatively obtain data-driven effective characteristics of seismic data.The tests with modelled data show that our approach attenuates most random noises and coherent noises and is superior to DnCNN in signal preservation.The network is further trained using transfer learning strategy and some observed data to obtain the ultimate denoising model.According to field data tests and performance evaluation from the perspectives of noise reduction and amplitude preservation,our approach is capable of suppressing random noises and surface waves to accurately recover effective signals;it also has advantages in low cost of processing and high efficiency.