A review of OBN processing: challenges and solutions

Zhang, Dongliang; Tsingas, C.; Ghamdi, Ahmed Al; Huang, Mengxing; Jeong, Woodon; Sliz, K.; Aldeghaither, Saud M; Zahrani, Saeed A

doi:10.1093/jge/gxab024

Cited by 12 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In OBS acquisition, two types of receiver arrangements are commonly used: ocean‐bottom cable (OBC) and ocean‐bottom node (OBN) acquisition, which involve different methods of receiver deployment with and without cables, respectively. Survey areas with islands inside were opened up using the OBN acquisition (D. Zhang et al., 2021). Achieving perfect coupling between the receivers and seafloor, whether OBC or OBN, is challenging.…”

Section: Model and Datamentioning

confidence: 99%

Integrated Algorithm for High‐Resolution Crustal‐Scale Imaging Using Complementary OBS and Streamer Data

Zand,

Górszczyk

2024

Earth and Space Science

View full text Add to dashboard Cite

We present an integrated algorithm for high‐resolution crustal‐scale imaging utilizing long‐offset wide‐angle ocean‐bottom seismometer (OBS) data and short‐offset multichannel streamer (MCS) data. The algorithm adopts a two‐step imaging strategy, initially using the OBS data to enhance deep structure imaging and capture long‐wavelength features of the migration velocity. Subsequently, the MCS data are migrated to recover detailed short‐wavelength components and shallow structures using the migration velocity model modified by the OBS result. Both steps employ the least‐squares reverse time migration (LSRTM) method with appropriate regularization techniques. The algorithm is implemented using the Bregmanized operator splitting (BOS) approach, known for its efficiency and adaptability in handling non‐smooth regularization, such as total‐variation (TV) constraints. To improve computational efficiency, compressed sensing is employed to store incident wavefields at half their actual size and reconstruct them accurately when required. Convergence is expedited through the use of preconditioners and the Anderson acceleration method. The proposed algorithm is validated through large‐scale numerical examples, demonstrating its robustness even with an initially imprecise velocity model. Results showcase improved resolution and accuracy achieved through the integration of OBS and MCS data. This study offers a comprehensive framework for crustal‐scale imaging, addresses computational complexities, and provides enhanced imaging capabilities.

show abstract

Section: Model and Datamentioning

confidence: 99%

Integrated Algorithm for High‐Resolution Crustal‐Scale Imaging Using Complementary OBS and Streamer Data

Zand,

Górszczyk

2024

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…OBN acquisition offers several advantages in contrast to the Towed Streamer (TS), Ocean Bottom Seismometer (OBS) and Ocean Bottom Cable (OBC) acquisition, such as the deployment flexibility of the system and the ability to record a wider azimuth and higher quality of seismic data. In view of the escalating challenges associated with a marine seismic survey and the continuous demand for high-quality seismic data, OBN acquisition has been recently used as an appealing alternative [1,2].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data

Chen,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal.

show abstract

“…It is typical to use filtering‐based method (Fomel, 2002; Jeong & Tsingas, 2019; Jiao et al., 2015; Liu & Fomel, 2013; Zhang et al., 2021) for attenuating seismic noise whose apparent velocity or other features differ from the signal of interest. Rank reduction, which is based on the fact that the noise will increase the rank of the matrix transformed from the noisy data, is also popular for seismic denoising in recent years (Chen & Sacchi, 2014, 2015; Jeong et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for noise attenuation from the ocean bottom node 4C data

Wang

Tan

Song

et al. 2023

Geophysical Prospecting

View full text Add to dashboard Cite

The source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections.

show abstract

A review of OBN processing: challenges and solutions

Cited by 12 publications

References 14 publications

Integrated Algorithm for High‐Resolution Crustal‐Scale Imaging Using Complementary OBS and Streamer Data

Integrated Algorithm for High‐Resolution Crustal‐Scale Imaging Using Complementary OBS and Streamer Data

Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data

Deep learning for noise attenuation from the ocean bottom node 4C data

Contact Info

Product

Resources

About