• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.174-186
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• 2009

When we apply a propagation model to the ocean with boundaries, we can calculate reflected wave using reflection coefficient suggested by Rayleigh assuming the boundaries are flat. But boundaries in ocean such as sea surface and sea bottom have an irregular rough surface. To calculate the reflection loss for an irregular boundary, it is needed to compute the coherent reflection coefficient based on an experimental formula or scattering theory. In this article, we derive the coherent reflection coefficients for a fluid-fluid interface using perturbation theory, Kirchhoff approximation and small-slope approximation respectively. Based on each formula, we can calculate coherent reflection coefficients for a rough sea surface or sea bottom, and then compare them to the Rayleigh reflection coefficient to analyze the reflection loss for a random rough surface. In general, the coherent reflection coefficient based on small-slope approximation has a wide valid region. Comparing it with the coherent reflection coefficients derived from the Kirchhoff approximation and perturbation theory, we discuss a valid region of them.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.6
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• pp.563-573
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• 2013

The ship radiated noise level fluctuates by the interference between direct and reflected paths. The effect of sea surface reflection path on interference depends strongly on sea surface roughness. This paper describes error characteristics of ship acoustic signature estimation by sea surface scattering effect. The coherent reflection coefficient which explains a magnitude of sea surface scattering and its resultant interference acoustic field is analyzed quantitatively as a function of a grazing angle, effective surface height, frequency, source-receiver range and depths of source and receiver. Theoretical interference acoustic field is compared with experimental result for two different sea surfaces and five different frequencies by changing source-receiver range. It is found that both matches well each other and a magnitude of interference acoustic field is decreasing by increasing a grazing angle, effective surface height, frequency, and depths of source and receiver and decreasing source-receiver range. For given experimental conditions, the transmission anomaly which is a bias error of ship acoustic signature estimation, is about a range of 1~3 dB. The bias error of an existing ship radiated noise measurement system is also analyzed considering wind speed, source depth and frequency.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.1 s.104
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• pp.39-44
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• 2006

This paper proposes a method of moments(MoM)/Monte-Carlo simulation and Physical Optics(PO) model to determine Bistatic Coherent Reflectivity of sea surfaces at various wind speeds. For the MoM simulation, a Gaussian random rough sea surface was generated based on the data of Tae-An ocean at various wind speeds and sea surface heights. The numerical results of the MoM/Monte Carlo simulations were used to verify the validity region of the PO model. It was found that the numerical result for a flat surface agrees quite well with the Fresnel reflection coefficient. The validity of the PO model on the rough sea surface is shown by using ray tracing method.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.