Marine Seismic Acquisition Research Articles

Low‐frequency seismic deghosting in a compressed domain using parabolic dictionary learning

AbstractDeghosting is an important technique in the marine seismic industry, as it plays a crucial role in mitigating the effects of ghost reflections from the sea surface, which can significantly impact the accuracy and resolution of subsurface imaging. In recent years, various acquisition‐based techniques have been developed to tackle the challenge of removing receiver–ghost reflections, which is the focus of our paper. These state‐of‐the‐art approaches, such as dual‐sensor or multicomponent towed streamer acquisitions, have demonstrated exceptional accuracy by combining pressure and particle motion data. However, such methods face limitations when dealing with low frequencies due to heavy noise contamination in the particle motion data. Consequently, ghost‐free data reconstruction at low frequencies typically relies on processing‐based approaches, which exclusively utilize recorded pressure data. This study presents a novel deghosting method for low‐frequency applications based on parabolic dictionary learning, which relies solely on recorded pressure data. The proposed method has the advantage of being applicable directly in a compressed domain, eliminating the need for data decompression prior to the deghosting process when compression is applied before the processing steps. This not only reduces costs related to data storage and transfer but also provides a cost‐effective alternative to conventional deghosting by operating directly on the compressed data format, which is smaller in size. The effectiveness of the proposed method was evaluated using both synthetic and field datasets. The results obtained from a synthetic data example indicate that the proposed method achieves similar results to an industry‐standard frequency–wavenumber method, while achieving a compression rate of over 7. Furthermore, the method was tested using a field dataset consisting of a full sail‐line of marine seismic acquisition. The comparison of 2D pre‐stack migrated images between the proposed method and the industry‐standard frequency–wavenumber method revealed insignificant differences, while achieving a compression ratio higher than 5 when our method was used.

Collaborative Seismic Environment Plan (CSEP) project – a long-term, consistent, consortium-based approach to environmental approvals

The Collaborative Seismic Environment Plan (CSEP) project was formed in 2018 by NERA (National Energy Resources Australia) and an industry consortium of marine seismic acquisition operators and exploration titleholders. Using a collaborative approach, the vision of the CSEP project was to streamline seismic survey environment plan preparation, submission and assessment by the regulator (National Offshore Petroleum Environmental and Safety Authority, NOPSEMA), with an emphasis on mutually agreed protocols with commercial fishers operating within the CSEP Operational Area. Stakeholder consultation undertaken during Environmental Plan (EP) preparation was identified by the consortium as a principal cause for concern and a common reason for unsuccessful EP submissions. The CSEP project has achieved the project’s vision through: A Commercial Fishing Industry Adjustment Protocol to provide a standardised, evidence-based process to assess and provide monetary adjustment to commercial fishers for loss of catch, displacement and gear loss/damage; an Operational Protocol to provide guidance regarding improved communications with commercial fishers, including the spatial and temporal repetition and advanced notifications of seismic activities; a single overarching approvals framework for seismic activities within the CSEP Operational Area; a consistent assessment of environmental receptors, potential impacts, and management controls for seismic activities; and Improved understanding of potential cumulative impacts of seismic activities across the CSEP Operational Area. The CSEP project is an example of best practice stakeholder consultation that can be applied to offshore oil and gas and renewable energy activities.

Geophysical Characterization and Attenuation Correction Applied for Hydrate Bearing Sediments in Japan Sea

Estimation of rock properties from seismic data is important for exploration and production activities in the petroleum industry. Considering the compressional velocity—the speed of propagating body waves in formations—and the quality factor (Q)—a measure of the frequency-selective energy losses of waves propagating through formations—both properties are usually estimated from multichannel seismic data. Velocity is estimated during multichannel processing of seismic reflection data in either the time or depth domain. In marine seismic acquisition, Q can be estimated from the following sources: Vertical Seismic Profile (VSP) surveys, where sources are located near the sea surface and geophones are distributed at depth along a borehole; and multichannel reflection data, where sources are also located near the sea surface and receivers are distributed either at the sea surface (conventional seismic survey with streamers) or on the sea floor (use of nodes or Ocean Bottom Cables (OBC)). The aforementioned acquisition devices, VSP, conventional streamers, nodes, and OBCs are much more expensive than single-channel acquisition with one receiver per shot due to the cost of operation. There are numerous old and new datasets from academia and the oil industry that have been acquired with single-channel acquisition devices. However, there is a paucity of work addressing the estimation of velocity and Q from this type of equipment. We investigate the estimation of Q and velocity from single-channel seismic data. Using the windowed discrete Fourier transform for a single seismic trace, we calculate the peak and dominant frequency that changes with time. In the geologic environment, higher frequencies are attenuated at shallow depths (time), while lower frequencies remain at deeper positions. From the rate at which higher frequencies are attenuated with time, we estimate the effective quality factor (Qeff). However, when using Kirchhoff migration to process single-channel seismic data, events far from the vertical projection of the receiver contribute to the trace at a given time. Then, an underestimation of the effective quality factor occurs. To compensate for the effects of more distant events with lower-frequency content contaminating the shorter events, we propose a linear equation to correct the effective quality factor estimated from migrated seismic data. Effective Q and its correction are estimated in five single-channel seismic lines surveyed along the Joetsu Knoll, a SW-NE anticline structure on the eastern margin of the Sea of Japan. These results are linked to geomorphological and geological features and the velocity field. Joetsu Knoll is a known site of massive gas hydrates (GH), which occur in the first hundred metres of Neogene sediments and, together with gas chimneys, play an important role in seismic wave absorption. Qeff estimated from migrated seismic data maintains the spatial relationship between high and low Q regions. The region of low Q, which is below 124 and has an average value of 57, occurs near the anticlinal hinge and tends to coincide with the region in which the Bottom Simulating Reflector (BSR) resides. The coexistence of GH and free gas coincides with the very low P velocity gradient of 0.225 s−1. BSR occurrence, Qeff and the geometry of the Joetsu anticline testify to progressive gas hydrate depletion northward along the dome.

Dual‐sensor wavefield separation in a compressed domain using parabolic dictionary learning

AbstractIn the marine seismic industry, the size of the recorded and processed seismic data is continuously increasing and tends to become very large. Hence, applying compression algorithms specifically designed for seismic data at an early stage of the seismic processing sequence helps to save cost on storage and data transfer. Dictionary learning methods have been shown to provide state‐of‐the‐art results for seismic data compression. These methods capture similar events from the seismic data and store them in a dictionary of atoms that can be used to represent the data in a sparse manner. However, as with conventional compression algorithms, these methods still require the data to be decompressed before a processing or imaging step is carried out. Parabolic dictionary learning is a dictionary learning method where the learned atoms follow a parabolic travel time move out and are characterized by kinematic parameters such as the slope and the curvature. In this paper, we present a novel method where such kinematic parameters are used to allow the dual‐sensor (or two‐components) wavefield separation processing step directly in the dictionary learning compressed domain for 2D seismic data. Based on a synthetic seismic data set, we demonstrate that our method achieves similar results as an industry‐standard FK‐based method for wavefield separation, with the advantage of being robust to spatial aliasing without the need for data preconditioning such as interpolation and reaching a compression rate around 13. Using a field data set of marine seismic acquisition, we observe insignificant differences on a 2D stacked seismic section between the two methods, whereas reaching a compression ratio higher than 15 when our method is used. Such a method could allow full bandwidth data transfer from vessels to onshore processing centres, where the compressed data could be used to reconstruct not only the recorded data sets, but also the up‐ and down‐going parts of the wavefield.

3D penta-source marine seismic acquisition: an innovative approach to address the sampling challenge

Penta-source acquisition is the latest addition to modern 3D Marine Seismic Acquisition Methods, a step ahead of the conventional dual and the more recent triple source acquisition methods. In the recent years, it has increasingly become popular due to its ability to deliver higher quality data enabled by finer cross-line sampling. This has been demonstrated through the Baxter Case Study (Hager et. all, 2016) where the 3D survey was successfully acquired and processed though the study area was relatively small. We present a case study from offshore Malaysia where penta-source acquisition method was adopted for the first time for a large 3D survey (~7000 sq.km) to enhance the data quality without compromising the acquisition efficiency. The survey was designed using a robust workflow to optimize acquisition parameters to meet the geophysical objectives, and executed safely and efficiently.

A dictionary learning method for seismic data compression

For a marine seismic survey, the recorded and processed data size can reach several terabytes. Storing seismic data sets is costly, and transferring them between storage devices can be challenging. Dictionary learning (DL) has been shown to provide representations with a high level of sparsity. This method stores the shape of the redundant events once and represents each occurrence of these events with a single sparse coefficient. Therefore, an efficient DL-based compression workflow, which is specifically designed for seismic data, is developed here. This compression method differs from conventional compression methods in three respects: (1) The transform domain is not predefined but data-driven, (2) the redundancy in seismic data is fully exploited by learning small-sized dictionaries from local windows of the seismic shot gathers, and (3) two modes (i.e., two different parameterizations) are proposed depending on the geophysical application. Based on a test seismic data set, we have determined superior performance of our workflow in terms of compression ratio (CR) for a wide range of signal-to-residual ratios, such as the zfp software or algorithms from the Seismic Unix package. Using a more realistic data set of marine seismic acquisition, we evaluate the capability of our workflow to preserve the seismic signal for different applications. For applications such as near-real-time transmission and long-term data storage, we observe insignificant signal leakage on a 2D line stack when the DL method reaches a CR higher than 20. For other applications, such as visual quality control of shot gathers, our method preserves the visual aspect of the data even when a CR of 95 is reached.

Super-sparse marine 3D: A game changer for seismic exploration

TotalEnergies SE and partners Shell and PetroSA recently completed the acquisition and processing of a large (10,000 km2) ultra-sparse (200 m between streamers) marine seismic acquisition survey off the west coast of South Africa in block 5/6/7 using a large PGS Titan Class Ramform vessel. The sparse design enabled fast acquisition and low survey cost and health, safety, and environment exposure. Advances in sparse processing enabled high-quality final seismic data consistent with the exploration objectives. In addition, DUG optimized the 4D regularization/interpolation parameters to approach the near offsets differently than the offsets with more complete coverage to help several processing steps. The survey was designed to be upgradable to a higher-resolution survey if needed via the addition of a custom regular infill pattern, either in its entirety or over targeted areas.

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

In 3D marine seismic acquisition, the seismic wavefield is not sampled uniformly in the spatial directions. This leads to a seismic wavefield consisting of irregularly and sparsely populated traces with large gaps between consecutive sail lines, especially in the near offsets. The problem of reconstructing the complete seismic wavefield from a subsampled and incomplete wavefield is formulated as an underdetermined inverse problem. We have investigated unsupervised deep learning based on a convolutional neural network for multidimensional wavefield reconstruction of irregularly populated traces defined on a regular grid. Our network is based on an encoder-decoder architecture with an overcomplete latent representation, including appropriate regularization penalties to stabilize the solution. We proposed a combination of penalties, which consists of the [Formula: see text]-norm penalty on the network parameters, and a first- and second-order total-variation penalty on the model. We determined the performance of our method on broadband synthetic data and field data represented by constant-offset gathers from a source-over-cable data set from the Barents Sea. In the field data example, we compare the results to a full production flow from a contractor company, which is based on a 5D Fourier interpolation approach. In this example, our approach displays improved reconstruction of the wavefield with less noise in the sparse near offsets compared with the industry approach, which leads to improved structural definition of the near offsets in the migrated sections.

Attenuation of long period multiple using F-k filter and surface-related multiple elimination methods on 2D broadband seismic data from Morowali Waters, Sulawesi

Wide range frequency bandwidth on seismic data is a necessity due to its close relation to resolution and depth of target. High-frequency seismic waves provide high-resolution imaging that defines thin bed layers in shallow sediment, while low-frequency seismic waves can penetrate into deeper target depth. As a result of broadband seismic technology, its wide range of frequency bandwidth is a suitable geophysical exploration method in the oil and gas industry. A major obstacle that is frequently found in marine seismic data acquisition is the existence of multiples. Short period multiple and reverberation are commonly attenuated by the predictive deconvolution method on prestack data. Advanced methods are needed to suppress long period multiple in marine seismic data. The 2D broadband marine seismic data from deep Morowali Waters, Sulawesi, contains both short and long period multiples. The predictive deconvolution, which is applied to the processing sequences, successfully eliminates short period multiple on prestack data. The combination of F-k filter and Surface Related Multiple Elimination (SRME) methods are successful in attenuating long period multiple of the 2D broadband marine seismic data. The Prestack Time Migration section shows fine resolution of seismic images.

Variable separations for sources and streamers in marine seismic acquisition: a novel towing concept to improve survey efficiency

This paper presents a novel acquisition methodology aimed at shallow targets and/or shallow water environments that require good near offset coverage and an improved crossline distance between sources and streamers. The proposed acquisition methodology features variable separations between adjacent sources and between adjacent streamers and builds on recent advances achieved by the seismic industry in towing the sources wide. This methodology results in better near offset coverage while improving survey efficiency, up to approximately 28% when compared to conventional configurations. A theoretical case study is presented to explain how to design this new towing concept and demonstrate the expected gains when compared to similar configurations.

A dictionary learning method with atom splitting for seismic footprint suppression

The acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. We have assumed that the time slice of 3D poststack migration seismic data mainly comprises two components: useful signals and the acquisition footprint. Useful signals describe the spatial distributions of geologic structures with local piecewise smooth morphological features. However, the acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in marine seismic acquisition appear as stripes. Because useful signals and the acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two subdictionaries to reconstruct these two components. We have devised an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D poststack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D poststack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that our method can effectively suppress the acquisition footprint with fidelity to the original data.

Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

In marine seismic acquisitions, signal interference remains a major menace. In this paper, a denoising approach based on the Variational Mode Decomposition (VMD) combined with the Hausdorff distance (HD) and Wavelet transform (WT) is proposed. There has been substantial research in this field over the years. However, traditional denoising methods fall short of achieving satisfactory results in an extremely low signal to noise ratio (SNR) environment. The feasibility, and stability of the proposed method was validated by performing simulations in MATLAB on both a synthetic signal and a seismic signal generated using real dataset. It was found that the proposed method does well in preserving marine signals in low SNR environments, and has a superior output SNR.

A review of OBN processing: challenges and solutions

Abstract In the last decade, a significant shift in the marine seismic acquisition business has been made where ocean bottom nodes gained a substantial market share from streamer cable configurations. Ocean bottom node acquisition (OBN) can acquire wide azimuth seismic data over geographical areas with challenging deep and shallow bathymetries and complex subsurface regimes. When the water bottom is rugose and has significant elevation differences, OBN data processing faces a number of challenges, such as denoising of the vertical geophone, accurate wavefield separation, redatuming the sparse receiver nodes from ocean bottom to sea level and multiple attenuation. In this work, we review a number of challenges using real OBN data illustrations. We demonstrate corresponding solutions using processing workflows comprising denoising the vertical geophones by using all four recorded nodal components, cross-ghosting the data or using direct wave to design calibration filters for up- and down-going wavefield separation, performing one-dimensional reversible redatuming for stacking QC and multiple prediction, and designing cascaded model and data-driven multiple elimination applications. The optimum combination of the mentioned technologies produced cleaner and high-resolution migration images mitigating the risk of false interpretations.

Adaptive de-blending of dithered simultaneous sources

In marine seismic data acquisition, de-blending multiple simultaneous sources is a challenging, undetermined problem and sparsity constraints are often used to derive a stable solution. In this article, we discuss the impact of additional adaptive constraints. In particular, we investigate conformity to a) direct wave modelling, b) ghost modelling and c) variations of signal-to-noise ratio. We find that these additional constraints improve convergence significantly. We demonstrate the performance of the proposed approach on a narrow-azimuth marine streamer survey acquired with triple-source simultaneous shooting and natural dithers. The proposed approach addresses the key de-blending challenges for this dataset which are protecting aliased steeply dipping events and avoiding signal damage.

Wi-Buoy: An Energy-Efficient Wireless Buoy Network for Real-Time High-Rate Marine Data Acquisition

The next frontier of maritime networking will see the deployment of large-scale buoy-based mesh networks, with an equal emphasis on both high-speed data transfer and energy-efficiency. One such challenging application is the operation of maritime seismic surveys for oil/gas exploration and academic studies. Large amounts of seismic data are generated at a rate of several Gigabits per second, by nearly 10,000–30,000 seismic sensors that are deployed on the seabed across an area of several hundred square kilometers in offshore oceanic environments. The task of monitoring existing reservoirs and identifying new oil and gas deposits require subsurface images of superior quality, which in turn are dependent on high-quality data for processing. Wireless technology can unlock real-time data transfer for rapid image-viewing, enhanced productivity, and reduced logistical costs. This interdisciplinary article outlines the challenges of marine seismic acquisition and the design of a buoy-based wireless backhaul network for high-rate data transfer over the ocean surface. Based on off-the-shelf IEEE 802.11 systems, a standards-compliant wireless buoy network architecture called Wi-buoy is proposed for real-time, scalable, and energy-efficient data delivery. In order to attain optimal power conservation, a Buoy-Based Power-Saving Backhaul (B-PSB) scheme is also proposed for specifying the operating parameters across all layers of the protocol stack. Essential aspects of the marine propagation environment are reviewed, and the performance of the proposed system is evaluated as a function of the antenna height, wind speed, compression ratio, and various flavors of the IEEE 802.11 standard. Furthermore, the use of Autonomous Underwater Vehicles (AUVs) is analyzed as an integral component of upcoming high-speed buoy-based networks in the maritime environment.

Quantitative Evaluation of Air Gun Leakage in Offshore Seismic Exploration

Air gun seismic source is widely used in marine seismic data acquisition, and air leakage is a common phenomenon. When there is an abnormal situation such as air gun leakage, and the quality control cannot be carried out according to the gun closing standard, the influence of the air leakage of a single air gun or multiple air guns on the entire gun array is considered from the aspects of near-field wavelet correlation and energy consistency. Finally, this paper has put forward the quantitative analysis and evaluation method for the whole gun array, which can provide quantitative evaluation reference for the excitation quality of air gun array.

Recent advances with wide-tow multi-sources in marine seismic streamer acquisition and imaging

Recent advances with wide-tow multi-sources in marine seismic streamer acquisition and imaging

Surface Diffraction Noise Attenuation for Marine Seismic Data Processing With Mathematical Morphological Filtering

Surface diffraction noise (SDN) is a kind of coherent noise and pervasive in marine seismic data acquisition. This kind of noise is intractable during seismic data processing. We present a new method based on mesh scanning and mathematical morphological filtering (MMF) to attenuate the SDN in marine surveys. The idea of mesh scanning will be first put forward to get the energy-map of the survey. Compared with the traditional mesh scanning method, the energy-map computing formulas will be improved to protect the reflections from the recognition of SDNs, especially the shallow reflections. All the possible surface diffractors will be shown on the computed energy-map with strong energy. Then, a 2-D mathematical morphological closing operation will be innovatively used to get the upper envelope of the energy-map and reduce the number of the possible surface diffractors with a maximal value picking. After confirming all the possible surface diffractors on the energy-map, the travel-time curves of SDNs could be calculated easily according to the kinematic propagation law of seismic wave for all the related shot gathers and the MMF will be used to extract all the SDNs along the trajectories of them within appropriate time windows. Both a real 2-D and a real 3-D marine seismic data will be processed by the proposed method to show the positive effects in attenuating the SDNs.

Cross-streamer wavefield reconstruction through wavelet domain learning

Seismic exploration in complex geologic settings and shallow geologic targets has led to a demand for higher spatial and temporal resolution in the final migrated image. 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 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 have investigated 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 that is used to reconstruct the wavefield across the streamers. We determine the performance of the proposed method on broadband synthetic data and TopSeis field data from the Barents Sea. From our synthetic example, we find 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 geologic model in the migrated section. In addition, we compare our method to an industry-standard Fourier-based interpolation method, in which the CNN approach shows an improvement in the root-mean-square (rms) error close to a factor of two. In our field data example, we find that the approach reconstructs the wavefield across the streamers in the shot domain, and it displays promising characteristics of a reconstructed 3D wavefield.

Extended use of receiver groups: Theory, synthetic example, and noise considerations

Receiver grouping is commonly used in marine towed-streamer seismic acquisition. Measurements from several receivers in a group are stacked to increase the signal-to-noise ratio of the resulting data and form an analog spatial antialiasing filter. I propose a method for extracting inline derivatives of the wavefield as additional measurements from the groups. This is achieved by multiplying the signal from the individual receivers in a group with predefined weights that corresponds to a finite-difference (FD) operator. The inline derivative(s) makes it possible to use multichannel sampling theorems to reconstruct the signal on a denser grid. Extraction of FD data from clusters of receivers is not a new concept, but I find that, by using the geometry of conventional streamer groups, it is possible to obtain FD data which are well suited for multichannel interpolation. The key to finding suitable FD operators is to recognize that it is not the ideal differentiation response we seek, but the impulse response of the group multiplied with the ideal differentiation response. Furthermore, under a Gaussian noise assumption, I derive formulas for the resulting noise level from sinc and higher order sinc interpolations. I find that the random noise level in the reconstructed data, when using higher order sinc interpolation, is expected to be higher than when using conventional sinc interpolation and will vary with respect to the distance from the original sampling points. The statistical analysis shows that it is beneficial to find FD operators with as small an [Formula: see text] norm as possible. A synthetic example shows that the proposed method of extracting FD operators and subsequent interpolation works very well. I foresee that the proposed method can be used to reduce the density of receivers (hydrophones or geophones) when designing new streamers or with existing equipment to improve the inline sampling.