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Seismic multiples in marine seismic data can affect the identification of oil and gas reservoirs. The efficiency of traditional multiple suppression methods, such as the Radon transform, depends on the accuracy of the velocity model for primaries and multiples, and the assumption of random background noise. To attenuate multiples with background noise, a method of primary reconstruction using a deep neural network based on data augmentation training is proposed. The designed deep neural network (DNN) includes convolutional encoding and decoding processes. The convolutional encoding process uses convolutional layers and maximum pooling layers to learn the features of the primaries, multiples, and background noise in a seismic data set. The convolutional decoding process uses these features to reconstruct the primaries and suppress the multiples and background noise. Using the data augmentation training (DAT) method in the training phase, the full-wavefield data and the predicted multiples are added to background noise and then rotated to constitute the augmented data sets. This allows the DNN to have a better multiple suppression effect and better robustness. A well-trained DNN can be used for primary reconstruction in the same work area directly or in other work areas with a transfer learning method. Three examples of synthetic data with two simple models and a Pluto model verify the effectiveness, efficiency, stability, and good generalization of the proposed method for primary reconstruction and multiple suppression. Another example from field data finds that the proposed method can efficiently suppress seismic multiples under complex conditions.

Summary The simultaneous source data obtained by simultaneous source acquisition contain crosstalk noise and cannot be directly used in conventional data processing procedures. Therefore, it is necessary to deblend the blended wavefield to obtain the conventionally acquired single-shot recordings. In this study, we propose an iterative inversion method based on the unsupervised deep neural network (UDNN) to deblend the simultaneous source data from a denser shot coverage survey (DSCS). In the common receiver gather (CRG), the coherent effective signals in the blended data of the primary and secondary sources are similar. We exploit the excellent nonlinear optimization capability of the U-net network to extract similar coherent signals from the blended data of the primary and secondary sources by minimizing the total loss function. The proposed UDNN method does not need to use the raw unblended data as label data, which solves the problem of missing label data and is suitable for deblending the simultaneous source data in different work areas with complex underground structures. One synthetic data and one field data examples are used to prove that the proposed method can suppress crosstalk noise and protect weak effective signals effectively, and achieve good effectiveness for the separation of simultaneous source data.

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Within the field of seismic data acquisition with active sources, the technique of acquiring simultaneous data, also known as blended data, offers operational advantages. The preferred processing of blended data starts with a step of deblending, that is separation of the data acquired by the different sources, to produce data that mimic data from a conventional seismic acquisition and can be effectively processed by standard methods. Recently, deep learning methods based on the deep neural network have been applied to the deblending task with promising results, in particular using an iterative approach. We propose an enhancement to deblending with an iterative deep neural network, whereby we modify the training stage of the deep neural network in order to achieve better performance through the iterations. We refer to the method that only uses the blended data as the input data as the general training method. Our new multi-data training method allows the deep neural network to be trained by the data set with the input patches composed of blended data, noisy data with low amplitude crosstalk noise, and unblended data, which can improve the ability of the deep neural network to remove crosstalk noise and protect weak signal. Based on such an extended training data set, the multi-data training method embedded in the iterative separation framework can result in different outputs at different iterations and converge to the best result in a shorter iteration number. Transfer learning can further improve the generalization and separation efficacy of our proposed method to deblend the simultaneous-source data. Our proposed method is tested on two synthetic data and two field data to prove the effectiveness and superiority in the deblending of the simultaneous-source data compared with the general training method, generic noise attenuation network and low-rank matrix factorization methods.