Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process. To solve this problem, this paper proposes a deep convolutional neural network denoising model based on noise estimation (MCD-DCNN). This model is primarily composed of two modules, the noise estimation module and the denoising module. The noise estimation module uses a multiscale convolutional neural network to better extract the characteristics of random noise in the seismic data. To make full use of the extracted features, a dense connection method is adopted between the multiscale convolutions in the noise estimation module. In the denoising module, we use multiscale convolutions and dense connections to replace the original convolutional neural network and use the residual structure (ResNet) and batch normalization (BN) to improve the denoising effect and running speed of the model. In this experiment, single trace and simple and complex profile data are used as input to simulate the real data processing environment. Finally, we compare the denoising effects of the MCD-DCNN model proposed in this paper with the current mainstream feed-forward denoising convolutional neural network (DnCNN) and a fast and flexible denoising convolutional neural network (FFDNet) models. The comprehensive results show that under the condition of a given prior noise level, the denoising performance of the FFDNet and MCD-DCNN models are comparable. In the absence of a priori noise level, the denoising performance of the FFDNet model drops sharply, while the denoising performance of MCD-DCNN is not affected; therefore, MCD-DCNN is more in line with actual seismic denoising.