Research On Seismic Signal Denoising And Reconstruction Methods Based On Deep Learning

In contemporary society,exploration technology has developed rapidly with the increasing dependence of humanity on oil and gas energy.During the actual exploration process,due to the influence of exploration technology,acquisition equipment,and field environment,the seismic signals collected manually are often subject to various noises and missing seismic traces.In order to conduct oil and gas exploration more accurately and improve exploration technology,this paper will implement seismic signal denoising and missing traces seismic signal reconstruction processing based on deep learning methods.To address the limitations of existing neural networks in feature extraction and weak model generalization capabilities,this paper investigates and proposes two neural network algorithms for seismic signal denoising and seismic signal reconstruction.The research content and achievements are as follows:1.In seismic signal processing,the attenuation of random noise is crucial in improving the signal to noise ratio of seismic signals.In order to effectively remove noise from seismic signals,this paper proposes an asymmetric convolution ADnCNN denoising network model,which is based on the DnCNN denoising network and combining the asymmetric convolution block.In order to verify the effectiveness of the ADnCNN network,this paper uses six traditional denoising methods,DnCNN network denoising and ADnCNN network denoising to form a control experiment to remove noise from simulated and publicly available seismic signals at different levels of random noise factors.The experimental results demonstrate that the ADnCNN method proposed in this paper can effectively remove random noise and retain effective seismic signals.2.In order to achieve seismic signal reconstruction accurately and solve the problems existing in traditional seismic signal reconstruction methods,this paper will firstly use UNet network to realize seismic signal reconstruction processing.Based on the UNet network,this paper proposes a residual double attention RDUNet network model,which combines residual learning and spatial channel attention mechanisms to further enhance the generalization ability of the model and reflect the continuity characteristics of seismic signals.In particular,spatial channel attention is introduced to the encoder part of the UNet network,and skip connections are used to mitigate the degradation of the network.The experimental results demonstrate that the proposed method in this paper has high reconstruction accuracy and is feasible and effective for seismic signal reconstruction.In summary,this paper uses the proposed ADnCNN network for seismic signal denoising and the RDUNet network for seismic signal reconstruction.At the same time,it forms multiple comparative experiments with traditional processing methods on simulated and publicly available seismic signals.Through experimental analysis and comparison,it can be concluded that the ADnCNN network proposed in this paper can better achieve seismic signal denoising,and the RDUNet network can better achieve seismic signal reconstruction,providing theoretical support for accurate exploration.