The presence of noise in towed marine seismic data is a long-standing problem. The various types of noise present in marine seismic records are never truly random. Instead, seismic noise is more complex and often challenging to attenuate in seismic data processing. Therefore, we examine a wide range of real data examples contaminated by different types of noise including swell noise, seismic interference noise, strumming noise, passing vessel noise, vertical particle velocity noise, streamer hit and fishing gear noise, snapping shrimp noise, spike-like noise, cross-feed noise and streamer mounted devices noise. The noise examples investigated focus only on data acquired with analogue group-forming. Each noise type is classified based on its origin, coherency and frequency content. We then demonstrate how the noise component can be effectively attenuated through industry standard seismic processing techniques. In this tutorial, we avoid presenting the finest details of either the physics of the different types of noise themselves or the noise attenuation algorithms applied. Rather, we focus on presenting the noise problems themselves and show how well the community is able to address such noise. Our aim is that based on the provided insights, the geophysical community will be able to gain an appreciation of some of the most common types of noise encountered in marine towed seismic, in the hope to inspire more researchers to focus their attention on noise problems with greater potential industry impact.

Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Both conventional marine seismic and wide azimuth data acquisition lack near offset coverage, which limits imaging in these settings. A new marine source over cable survey, with split-spread configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, wavefield reconstruction in the near offsets is challenging in the shallow part of the seismic record due to the high temporal frequencies and coarse sampling that leads to severe spatial aliasing. We investigate deep learning as a tool for the reconstruction problem, beyond spatial aliasing. Our method is based on a convolutional neural network (CNN) approach trained in the wavelet domain in order to reconstruct the wavefield across the streamers. We demonstrate the performance of the proposed method on broadband synthetic data and TopSeis field data from the Barents Sea. From our synthetic example, we show that the CNN can be learned in the inline direction and applied in the crossline direction, and that the approach preserves the characteristics of the geological model in the migrated section. In addition, we compare our method to an industry-standard Fourier-based method, where the CNN approach shows an improvement in the RMS error close to a factor of two. In our field data example, we show that the approach manages to reconstruct the wavefield across the streamers in the shot domain, and displaying promising characteristics of a reconstructed 3D wavefield.

The 4D/5D interpolation and regularization methods effectively improve the quality of seismic imaging. In addition to the Fourier domain interpolation method, 5D interpolation based on the common reflection surface (CRS) method has attracted more and more attention due to simplicity of its implementation and effectiveness of performance. However, the main challenge of this method is the heavy calculation in parameter estimation. To overcome this limitation, we introduce a kinematic wavefield attributes based prestack data interpolation and regularization method. This method uses gradient structure tensor (GST) and quadratic structure tensor (QST) methods to extract kinematic wavefield attributes (local slopes and curvatures) and use them for fast 3D zero-offset (ZO) CRS parameter estimation. The derived parameters are then used for 3D CRS based prestack interpolation and regularization. The proof of concept is demonstrated on datasets acquired by TopSeis. The corresponding results show that the improved efficiency of the GST/QST based method in kinematic wavefield attribute extraction and 3D ZO CRS parameter estimation. Moreover, the interpolated and regularized TopSeis prestack data derived from the subsequent 3D ZO CRS proves the simplicity and effectiveness of this method.

Regularization and interpolation of 3D offset classes prior to imaging are an important and challenging step in the marine seismic data processing flow. Here we describe how to perform this task using a deep neural network, and we explain how to overcome the challenge of creating a suitable training data set. The training data set is generated by de-migrating stacked pre-stack depth migration images. For each offset class volume, we de-migrate the pre-stack depth migrated stacked image into two configurations: (i) the original survey configuration consisting of the recorded source/receiver positions and (ii) an 'Ideal' survey configuration with constant offset and azimuth for each 3D offset class. The training creates a 3D convolutional encoder-decoder model that will regularize and interpolate seismic data. The convolutional encoderdecoder is trained on 3D sliding windows in each 3D offset cube to map from (i) to (ii), i.e. to map the original survey configuration with irregular and sparse sampling into the fully sampled regular offset cubes suitable for offset-based migration, such as Kirchhoff migration. Such migration algorithms rely on regular and sufficiently dense sampling to achieve constructive interference to image the structures and destructive interference to suppress migration noise. We test the new method on one synthetic and one field data example and show that it performs better than a standard regularization/interpolation method based on anti-leakage Fourier transform, especially for the smallest offset classes. On the synthetic data, we also demonstrate that the convolutional encoder-decoder method preserves the amplitude versus offset as well as the standard method.