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The oscillatory bubble pulses generated by airguns in seawater are known to produce artefacts in seismic images. Although such artefacts are suppressible by employing larger airgun arrays in acquisition, small airgun arrays are used more often now to minimize the environmental impacts, thus raising the need for further suppressing bubble pulses at data processing stage. For a deep marine reflection dataset recently acquired by a small airgun array, we compare the effectiveness of three popular debubbling methods that estimate the far‐field source signature based on theoretical simulation and wavelets extracted from seafloor reflections and direct arrivals, respectively. In this case, due to the lack of near‐field measurements for calibration, the debubbling via simulation underperforms the two wavelet extraction methods. Overlapping events in the noisy response of seafloor sediments lead to the failure of the wavelet extraction from primary seafloor reflections. The estimated source signature based on direct arrivals achieves the best bubble suppression result, indicating the importance of signal‐to‐noise ratio and a low level of directionality of the small airgun array source. [ABSTRACT FROM AUTHOR]

The quality of seismic migration images strongly depends on accurate velocity models. However, migration velocity models often contain errors causing artifacts which degrade seismic resolution and fidelity. Here, we propose a method to enhance seismic migration images using processes in angle-domain common-image-gathers (ADCIGs), without requiring modification to the velocity model. The ADCIGs impacted from velocity errors show non-flat gathers and scattered noise. To enhance the imaging quality in ADCIGs, these problems need to be treated prior to stacking. Ideally, layers in ADCIGs should be flat without smearing amplitudes through all angles. Using this principle, we iteratively process one reflection at a time by isolating it into a local window and then flattening it. Also, we apply internal processing steps to enhance the signal. The algorithm for segregating a reflection, referred to as the connected-component labeling (CCL) method, is the primary method for extracting any feature that has a continuous form within the ADCIGs. Moreover, this method is insensitive to scattered noise so it is a suitable approach for classifying reflections or noise. To test the efficiency of this algorithm, we create ADCIGs containing relatively poor-quality images using an inaccurate migration velocity model. The primary objective of this test is to show how well the CCL method can select various forms of reflections in the presence of various levels of noise. The results show that the CCL method is capable of individually decomposing a reflection, which can facilitate processing in the internal workflow. Finally, we evaluate the quality of processed migration images by comparing our processed Poynting-vector reverse time migration (PVRTM) images with reverse time migration applied Laplacian filter and partial stacked PVRTM. These comparable migration images are benchmarked against a baseline synthetic reflector model. The essential improvements of our method are addressed by the migration sections, namely the removal of diffraction artifacts at both small and large scales and the improvement of phase coherency. The final stacked migration image produced with our methods shows clearer geological features that are capable of delivering a more reliable seismic interpretation. [ABSTRACT FROM AUTHOR]

Variations in the topography and thickness of the near-surface low-velocity weathering zone negatively impact the quality of onshore seismic data and images. Such impact can be mitigated using accurate near-surface velocity models. We propose to estimate near-surface velocities using a layer-cell tomography method which sequentially combines the model parameterizations of multiscale deformable-layer tomography (DLT) and cell tomography. We first estimate the long-wavelength velocity variations using multiscale DLT, which maps the undulating geometry of the weathering base where the angular coverage of first-arrival raypaths is limited. Then we convert the solution model of multiscale DLT into a cell model, and further determine the short-wavelength velocity variations using cell tomography. We test the proposed layer-cell tomography method using synthetic datasets and a field dataset. The results indicate that the new method improves the resolution of the velocity model from the multiscale DLT, and reduces the artifacts in the velocity solution compared to those observed in the cell tomography method. Reverse time migration of the synthetic data shows that using the velocity model from the layer-cell tomography method gives more accurate images of reflectors. For the field dataset test, the layer-cell tomography method yields a high-resolution near-surface velocity model that could be validated by the improvements in the tomostatic correction results. [ABSTRACT FROM AUTHOR]

Using directional waves is advantageous in subsurface seismic imaging because such waves are localized in space and time. Hence, the recorded data contain information mostly about the illuminated targets. However, most seismic field data are physically generated by point sources that excite wavefield propagating into all directions and interact with all parts of the medium. How to convert point-source wavefields into a wavefield due to a directional wave packet without physically exciting it, is the subject of investigation here. In particular, we investigate the Gaussian wave packet (GWP) which is a directional wave packet localized in space and time. GWP is an exact solution of the wave equation and it differs from the widely used asymptotic solutions such as Gaussian Beams or Gaussian Packets. The spatial localization and propagation direction of GWP are controlled by parameters chosen by users. We propose a method to synthesize GWP field data in complex media using recorded shot records of point sources based on the reverse-time concept. To assess the quality of the synthesized GWP data, we study the influences from point-source wavelets and the spatial interval between point sources. Finally, we present the constructed GWP fields in two numerical examples. We also show one application of the GWP data in seismic imaging using multiples. [ABSTRACT FROM AUTHOR]

Reverse time migration (RTM) is a seismic imaging method to map the subsurface reflectivity using recorded seismic waveforms. The practice in exploration seismology has long established a two-fold approach of seismic imaging: Using velocity modeling building to establish the long-wavelength reference velocity models, and using seismic migration to map the short-wavelength reflectivity structures. Among various seismic migration methods for different situations, RTM is the only method that is capable to use all seismic wave types that can be computed numerically. Being initiated in early 1980's, RTM seeks an image of the subsurface reflectivity as the best match in an image space between the extrapolation of time-reversed waveform data and the prediction based on estimated velocity model and source parameters. Judging the image quality in the same space of forming the images is more advantageous than the approaches of modeling and inversion which seek the solution in the model space but judge its fitness in data space. Considering that most seismic migration applications today still use primary reflection as the only signal, the capability of RTM to use all computable wave types is unique and helpful reducing the imaging artifacts due to mistaking non-primary waves as primary reflections. Hence, we refer to those RTM algorithms using only primary reflections as the first-generation RTM methods, and the RTM algorithms making a full use of primary reflections, multiple reflections and other non-primary waveform data as the second-generation RTM methods. This paper reviews the development history of the RTM along with its major challenges, current solutions, and future perspectives. [ABSTRACT FROM AUTHOR]

Least-squares migration (LSM) is a data-fitting imaging approach seeking the seismic reflectivity image of the most accurate amplitude and optimal resolution. However, the high computational cost of LSM has hindered its broad application. In this study, we combine a convolutional neural network (CNN) with LSM to significantly improve the computational efficiency while retaining the imaging quality. Taking CNN as a "projector," we treat LSM as the "projection" from the ordinarily migrated images to the least-squares updated images. We conduct this CNN-assisted LSM in the shot gather domain using a Gaussian beam migration and the corresponding LSM. The training data for CNN consist of 10–15% of all shot gathers, with the Gaussian beam migrated shot gathers as the input and the LSM shot gathers as the target. After the training, the processing time for the remaining shot gathers took several minutes for 2D cases. The results from testing with the Sigsbee 2B synthetic dataset and a field marine dataset indicate the CNN-assisted LSM saved 80–90% of the computation time of the full LSM and achieved significantly higher image fidelity than that of the ordinary migration. [ABSTRACT FROM AUTHOR]

Seismic illumination plays an important role in subsurface imaging. A better image can be expected either through optimizing acquisition geometry or introducing more advanced seismic migration and/or tomographic inversion methods involving illumination compensation. Vertical cable survey is a potential replacement of traditional marine seismic survey for its flexibility and data quality. Conventional vertical cable data processing requires separation of primaries and multiples before migration. We proposed to use multi-scale full waveform inversion (FWI) to improve illumination coverage of vertical cable survey. A deep water velocity model is built to test the capability of multi-scale FWI in detecting low velocity anomalies below seabed. Synthetic results show that multi-scale FWI is an effective model building tool in deep-water exploration. Geometry optimization through target oriented illumination analysis and multi-scale FWI may help to mitigate the risks of vertical cable survey. The combination of multi-scale FWI, low-frequency data and multi-vertical-cable acquisition system may provide both high resolution and high fidelity subsurface models. [ABSTRACT FROM AUTHOR]

For full-waveform inversion (FWI) with sparse survey layout, it is crucial to optimize the source and receiver geometry toward full-wave illumination. We propose a sparse geometry optimization method and workflow for target-oriented FWI using finite-frequency illumination analysis. The frequency-dependent impact of a source-receiver pair to the wavefield sensitivity kernel and diagonal terms of approximate Hessian matrix are derived under visco-acoustic model assumptions. Based on the reciprocity principle, we use virtual sources in target zone to efficiently calculate the Green's function and illumination from acquisition surface. Optimal locations of sparse receivers are determined by comparing band-limited illumination at reference stations and their surrounding region through an illumination attribute scan. FWI model residual errors are used to evaluate the performance of optimized geometry layout for a given target zone. The performances of different geometries are compared to determine a suitable frequency band for FWI geometry optimization. Synthetic deep water model and ocean bottom node acquisition geometry are used to verify the feasibility of the workflow. Numerical tests in target-oriented FWI show that optimized geometry with constraints of band-limited illumination below10 Hz has better performance than that above 10 Hz. An extension of our geometry optimization method to sparse source configuration is straightforward. • Target-oriented acquisition experiment design. • Finite-frequency illumination analysis. • High resolution multi-scale seismic imaging. • Environment friendly acquisition planing. • Deep-water velocity modeling. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is an advanced seismic imaging method for determining high resolution velocity models of different scales, such as in subseafloor geo-hazard monitoring and risk analysis. Among all two-way wave-equation based modeling methods, a mixed-grid finite-difference modeling (FDM) in frequency domain has the advantage of low numerical dispersion errors for mesh size up to 1/4 wave-length. However, irregular source and receiver positions situated away from the FDM nodes introduce position approximation error (PAE) as large as half mesh size. When a source or receiver is situated in strong heterogeneity or near a free surface, the PAE induces imaging artifacts in high frequencies. We propose an imaging artifact suppression method for multi-scale FDM and FWI in marine seismic survey. The delta function of the source or receiver position is approximated by a discrete Kaiser-windowed sinc function in unbounded medium. The free-surface effects to sources and receivers are compensated with the mirror source concept. The final coefficients for delta function approximation and wavefield interpolation are spatially confined tensor products. The results using ocean bottom cable and vertical cable survey layouts in synthetic models validate the accuracy and feasibility of this new FDM and FWI strategy. The FWI tests shows that this strategy has reduced small scale artifacts at high frequencies for survey layouts of good and poor illuminations. The extension of our method to 3-D visco-acoustic model or reverse time migration is straightforward. • High resolution subseafloor geo-hazard monitoring. • Multi-scale visco-acoustic seismic inversion. • Seismic imaging artifacts suppression. • Positioning errors in marine environment. • Velocity model building for shallow marine strata. [ABSTRACT FROM AUTHOR]