Simultaneous Seismic Data Denoising and Reconstruction With Attention-Based Wavelet-Convolutional Neural Network

The knowledge of hidden resources present inside the Earth layers is vital for the exploration of petroleum and hydrocarbons. However, the recorded seismic data are noisy and incomplete with missing traces that leads to misinterpretation of the Earth layers. In this manuscript, we consider seismic data with Gaussian, non-Gaussian noise distribution, regular, and irregular missing traces. We propose a method for simultaneous noise attenuation and reconstruction of the incomplete seismic data with attention-based wavelet convolutional neural network (AWUN). The wavelet transform is used as pooling layer and inverse wavelet transform (IWT) is used for upsampling layers to avoid information loss. The attention module is used to obtain weights for various feature channels with higher weights assigned to the more significant information. In addition, we propose to use hybrid loss function (logcosh + huberloss) to denoise and accurately reconstruct the seismic data. Moreover, the effect of various hyperparameters in the training process of convolutional neural networks (CNNs) is studied. Further, we tested the performance of proposed method on synthetically generated data and field data examples. The quantitative results demonstrated that our proposed deep learning (DL) method has shown improved signal-to-noise ratio (SNR) and mean-squared error (mse) when compared to the existing state-of-the-art methods.