A new method for OBS relocation using direct water-wave arrival times from a shooting line and accurate bathymetric data

Liu, Hongwei; Liu, Huaishan; Xing, Lei; Li, Qianqian

doi:10.1007/s11001-022-09482-0

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…The proposed method is applied to field OBS data to illustrate its applicability in cases with low energy levels and low SNR data. The first selected data set (Liu et al., 2022) was acquired in Papua New Guinea with an airgun, and the maximum offset was 85 km. The excitation interval was 90 s, and the sampling interval of the OBS was 20 ms. To improve the SNR, the data were preprocessed.…”

Section: Methodsmentioning

confidence: 99%

A first‐arrival wave recognition method based on the optimal dominant energy spectrum

Liu

et al. 2022

Geophysical Prospecting

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Refraction seismic data acquired by wide‐angle ocean‐bottom seismometers are typically used to invert geological structures. Accurate and reliable first‐arrival traveltime picks are therefore critical to successful refraction data processing. Unfortunately, signal‐to‐noise ratios of refraction data acquired by wide‐angle ocean‐bottom seismometers at far offsets are usually low; thus, conventional methods such as the energy ratio method and correlation analysis method usually fail to accurately and efficiently determine the refraction phase. We therefore develop a novel first‐arrival wave recognition method based on the optimal dominant energy spectrum to improve the accuracy of refraction wave picking on wide‐angle ocean‐bottom seismometer data. The method consists of three steps. The first step is to obtain the optimal dominant energy spectrum according to the power spectrum density of sample scanning. Second, the initial position of the first‐arrival wave is recognized by combining the range of the optimal dominant energy spectrum and the maximum point of its derivative. Third, the modified cross‐correlation method adjusts the initial position to obtain a more accurate arrival time. In this paper, we illustrate the workflow and feasibility of the proposed method via testing on model data. Then, the method is used to determine the first‐arrival time of field wide‐angle ocean‐bottom seismometer data acquired in the Southwest Pacific Ocean and Xisha Islands. The results show that our method can accurately pick the refraction phase at offsets as far as 80 km, thus significantly reducing the uncertainties in first‐arrival phase picking.

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Section: Methodsmentioning

confidence: 99%

A first‐arrival wave recognition method based on the optimal dominant energy spectrum

Liu

et al. 2022

Geophysical Prospecting

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“…Due to the influence of the SNR of the data and the decision criterion of the waveform starting point, several errors impact seismic traveltime results (Liu et al, 2022). Referring to the suggestions of Zhang and Toksöz (1998) and Korenaga et al (2000), the overall traveltime error should be divided into two types: common phase errors and individual errors.…”

Section: Uncertainty Of the Traveltimementioning

confidence: 99%

A robust array geometry inversion method for a deep-towed multichannel seismic system with a complex seafloor

Li,

Pei,

Liu

et al. 2023

Front. Mar. Sci.

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Deep-towed multichannel seismic exploration technology has better applicability and more development potential when utilized to invert the geoacoustic properties of deep-sea sediment. The accurate geometric inversion results of the receiving array are crucial for fine submarine sediment imaging and physical property parameter inversion based on deep-towed multichannel seismic data. Thus, this study presents an array geometry inversion method suitable for complex seafloors to address the challenge of precise source-receiver positioning. The objective function of the deep-towed seismic array geometry inversion is built using the shortest path algorithm according to the traveltimes of direct waves and seafloor reflections, and the particle swarm optimization algorithm is used to achieve high-precision inversion of the source-receiver position. The results showed that the proposed method is shown to have incomparable applicability and effectiveness in obtaining exact source-receiver positions for deep-towed multichannel seismic systems. Regardless of the complexity of the seabed morphology, seismic image processing techniques using the source-receiver position data obtained by the suggested method produce fine seismic imaging profiles that clearly and accurately reflect the structural characteristics of sediments. These findings provide insights for the accuracy and reliability of the proposed geometric shape inversion method for deep-towed seismic arrays in practical applications to meet the requirements of near-bottom acoustic detection for fine imaging of deep-sea seabed strata and precise inversion of geoacoustic parameters.

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“…This chapter mainly shows the denoising ability of the StD-DAE on actual OBS data. We apply the proposed method to an OBS data set from the Western Pacific with strong random noise (Liu et al, 2022). The original OBS data contain 950 source points, the sample interval is 4 ms and the time length is 4 s. We select two parts of the OBS data, including the main first break information from near offset and far offset, for the test, as shown in Figures 7 and 9.…”

Section: Field Examplesmentioning

confidence: 99%

Random noise attenuation of ocean bottom seismometers based on a substep deep denoising autoencoder

Lin

Xing

et al. 2023

Geophysical Prospecting

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Ocean bottom seismometer data usually contain a large amount of random noise, which seriously reduces the signal‐to‐noise ratio of the data and affects subsequent imaging. Hence, random noise attenuation is one of the most essential steps in ocean bottom seismometer data processing. In this paper, a novel approach is proposed to attenuate the marine seismic random noise of ocean bottom seismometers based on a six‐dense‐layer denoising autoencoder. We input the domain data into the denoising autoencoder, the encoder compresses the signal and noise and extracts the main features and the decoder finally reconstructs the denoised data with the same dimension as the input. In this approach, because few raw labelled examples are available, we first constructed the pretraining, training and test data sets by patch processing. Then, we pretrained the encoder based on clean synthetic seismic data through unsupervised learning and pretrained the decoder based on noisy synthetic seismic data through supervised learning. Next, the pretrained model was fine‐tuned with the encoder–decoder on a raw seismic data set in an unsupervised manner. Finally, we used the model to attenuate the random noise in raw ocean bottom seismometer data for testing. Synthetic and raw examples are used to compare the deconvolution, multichannel singular spectrum analysis, deep denoising autoencoder and substep deep denoising autoencoder approaches. Experimental tests demonstrate that the proposed method has higher processing efficiency and precision.

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A new method for OBS relocation using direct water-wave arrival times from a shooting line and accurate bathymetric data

Cited by 6 publications

References 21 publications

A first‐arrival wave recognition method based on the optimal dominant energy spectrum

A first‐arrival wave recognition method based on the optimal dominant energy spectrum

A robust array geometry inversion method for a deep-towed multichannel seismic system with a complex seafloor

Random noise attenuation of ocean bottom seismometers based on a substep deep denoising autoencoder

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