Adaptive Subtraction Based on U-Net for Removing Seismic Multiples

Li, Zhongxiao; Sun, Ningna; Gao, Haotian; Qin, Ning; Li, Zhenchun

doi:10.1109/tgrs.2021.3051303

Cited by 27 publications

(7 citation statements)

References 36 publications

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“…They treated adaptive SS as a nonlinear problem and minimized network parameters during training to mitigate overfitting. Through testing on a data set, their method achieved a remarkable 20.5 dB improvement in SNR, confirming the effectiveness of their research (Li Z. et al, 2021b). Yadav and Mehta (2021) discussed the impact of DC bias in DC transmission lines on transformers, leading to increased noise and vibration.…”

Section: Related Worksupporting

confidence: 52%

“…Experimental results revealed that this method outperformed most baseline methods, particularly at low SNR (Signal-Noise Ratio, SNR) levels (Li C. et al, 2021a). In the field of petroleum seismic exploration, Li Z. et al (2021b) conducted research on the repeated removal process of seismic waves, which proved crucial for underground structure imaging. They treated adaptive SS as a nonlinear problem and minimized network parameters during training to mitigate overfitting.…”

Section: Related Workmentioning

confidence: 99%

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Detection technology application based on spectral subtraction and vibro acoustic principle in the measurement of ship reliability level

Jin

2024

Front. Mech. Eng.

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Introduction: As an important part of ship manufacturing, parts are of great significance in the calculation of its reliability level.Methods: To achieve rapid damage detection of ship parts, a method for measuring the reliability level of ship casting and forging parts based on spectral subtraction and vibration-acoustic principles was proposed. This method improves the spectral subtraction method by adding a percussion vibration signal and time-frequency analysis, and uses the principle of resonance acoustics to complete the construction of the test platform to obtain the natural frequency of the part and achieve non-destructive testing of the part.Results: The results show that using the Fabric data set as the task data set for experiments, the accuracy of the research method is 98.54%; the uncertainty is 5.58; the sensitivity detection is 0.26 μm. In the comparison of the spectrogram of the sound signal after noise reduction, this method has fewer yellow spots remaining on the spectrogram of the noise reduction sound signal, and almost no excess noise remains. In the comparison of modal simulation data of ship parts, the maximum relative error between the simulation data obtained by this method and the natural frequency value obtained from the experimental data is 2.3%, and there is no value exceeding 2.5%, so the error is small.Discussion: The above results show that this method can obtain more accurate natural frequencies of parts, can effectively calculate the reliability level of ship casting and forging parts, and provides a new method reference for the safe operation of ships.

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Section: Related Worksupporting

confidence: 52%

Section: Related Workmentioning

confidence: 99%

Detection technology application based on spectral subtraction and vibro acoustic principle in the measurement of ship reliability level

Jin

2024

Front. Mech. Eng.

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“…For example, (1) the sound of piling during the construction of wind turbine infrastructure may cause damage to the hearing of marine animals; (2) the noise of the wind turbine may affect the communication or sense of direction of marine animals or fish, causing them to get lost; (3) in the process of offshore wind power construction and maintenance, the operation of vessels may also interfere with the habitat of marine fish. Figure 6 shows the underwater acoustic modem (IOISAS Seatrix), which can be used in underwater communication, earthquake monitoring, biological monitoring, and other fields [40, 93,94]. Figure 7 shows the underwater junction box observation network system, which mainly includes the docking of underwater vehicles, data communication relay, underwater data acquisition, control command transmission, etc.…”

Section: Sea Floor Monitoringmentioning

confidence: 99%

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Kou

Zhang

et al. 2022

Sensors

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In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

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“…Wang et al [38] proposed a self-supervised deep neural network method based on a hybrid loss function including localized wavefield characteristics to remove multiple waves. Li et al [39] applied an adaptive subtraction method based on U-Net to represent the complex nonlinear relationship between real and modeled multiples.…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

et al. 2024

Applied Sciences

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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.

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Adaptive Subtraction Based on U-Net for Removing Seismic Multiples

Cited by 27 publications

References 36 publications

Detection technology application based on spectral subtraction and vibro acoustic principle in the measurement of ship reliability level

Detection technology application based on spectral subtraction and vibro acoustic principle in the measurement of ship reliability level

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

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

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