Optical fiber marine seismic exploration system feasibility study

Wang, Xiangchun; Li, Xueliang; Zhang, Haibing; Xiao-zhu, Hao; Yu, Kaiben

doi:10.1007/s11600-019-00333-y

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…3/e f f (n) (8) Equations (9) are derived by incorporating laboratory data (u 0 = 390 V, R = 0.9 Ω, and R 0 = 0.25 Ω) into Equation (4).…”

Section: Methods To Solve Equationmentioning

confidence: 99%

“…( ) = (−0.03395 + 91.75)/100 (6) According to measured values, the power consumption of the fiber package (PCFP) is 2.5 W and the power consumption of DAS (PDAS) is 2.3 W. Using these values, we derived Equations (7) and (8).…”

Section: Methods To Solve Equationmentioning

confidence: 99%

“…Sensor networks in the streamer collect seismic waves reflected from the submarine strata. After being processed, the original record information is used to infer the underground structure of the subsea strata [8][9][10][11][12]. The working process is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Research on Power System of Distributed Sensor Networks for Long Streamers in Marine Seismic Exploration

Song

et al. 2019

Sensors

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The electric power system plays an important role in sensor networks. In the marine seismic exploration streamer system (MSESS), an underwater power system transmits high-voltage direct current to all nodes in the streamer through a daisy chain structure. As offshore oil exploration develops toward deep water, it is necessary to study long streamers with large-scale sensor networks for deep water exploration. When the length of a streamer is increased to a certain value, the output current of the power supply increases sharply. This results in the activation of the overcurrent protection and the power supply shuts down. This paper puts forward an accurate model for an underwater power system applied to MSESS. Using the Newton iteration algorithm and a reverse algorithm, equations established by the model are solved and laboratory test results are used to verify the accuracy of the model. Based on simulation and analysis of the model, we explain why the power system crashes when the streamer is too long. Software that can quickly calculate the maximum number of nodes (the maximum length with which the system works normally) is developed and it is significant for the design of MSESS. The method of research could also be applied to relevant work such as large-scale sensor networks with daisy-chaining power supply in land seismic exploration.

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“…3/e f f (n) (8) Equations (9) are derived by incorporating laboratory data (u 0 = 390 V, R = 0.9 Ω, and R 0 = 0.25 Ω) into Equation (4).…”

Section: Methods To Solve Equationmentioning

confidence: 99%

Section: Methods To Solve Equationmentioning

confidence: 99%

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Modeling and Research on Power System of Distributed Sensor Networks for Long Streamers in Marine Seismic Exploration

Song

et al. 2019

Sensors

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Research on Noise Suppression Technology of Marine Optical Fiber Towed Streamer Seismic Data Based on ResUNet

et al. 2022

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Optical fiber seismic exploration technology has been widely used in marine oil and gas hydrate exploration due to its wide frequency band and high sensitivity. However, there are more types of noise in the collected data by optical fiber hydrophone than by a conventional piezoelectric seismic exploration system. Considering that the conventional denoising method is time-consuming, this paper proposes a convolutional neural network (CNN) and a ResUNet network based on deep learning to suppress the noises. ResUNet is improved on the basis of CNN; it is composed of a feature extraction part, a feature reconstruction part and a residual block. Both CNN and ResUNet networks achieved obvious denoising effects on optical fiber towed streamer seismic data and improved the signal-to-noise ratio of data effectively. The ResUNet network has better denoising effects than CNN, even better than conventional denoising methods. The ResUNet network can solve the problem of gradient disappearance caused by network deepening; it recovered edge data well, and it has high efficiency compared with conventional denoising methods. Two evaluation indexes, relative error (RE) and similarity structure degree (SSIM), were introduced to compare the denoising effect of the ResUNet network with that of CNN. The experimental results showed that the performance of the ResUNet network in these two aspects is better than that of CNN.

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Optical fiber marine seismic exploration system feasibility study

Cited by 2 publications

References 4 publications

Modeling and Research on Power System of Distributed Sensor Networks for Long Streamers in Marine Seismic Exploration

Modeling and Research on Power System of Distributed Sensor Networks for Long Streamers in Marine Seismic Exploration

Research on Noise Suppression Technology of Marine Optical Fiber Towed Streamer Seismic Data Based on ResUNet

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