• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.645-651
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• 2003

This paper presents a revised beamforming inversion method for ocean acoustic tomography. In the proposed inversion method, the relation between group velocity and phase velocity that are the characteristics of the waveguide is used for the inversion of perturbed sound speed profile. The group velocity and phase velocity can be expressed as a function of the travel time and arrival angle of the received signals that are analyzed by the beamforming signal processing. This paper illustrates the simulated results of inversion for the fluctuated sound speed profile of the East Korea Sea and we found the applicability of revised beamforming inversion method to range independent ocean.

• Economic and Environmental Geology
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• v.29 no.5
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• pp.629-637
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• 1996

In this paper a numerical experiment is conducted to determine the low acoustic impedance of a thin oil or gas reservoir from a seismogram by using the generalized linear inversion method. The seismograms used are normal incident synthetic seismograms containing p-wave primary reflections, multiples, and peg-leg multiples on the layers consisting of oil-, gas-, water-filled sandstone incased in shales. In this experiment the acoustic impedance, the location of reservoir boundary, thickness, and source wavelet are assumed initially and revised iteratively by the least-squares-error technique until the difference between the seismogram and calculated one is very small. This experiment shows that the acoustic impedance and thickness, about 10 m thick, can be determined by the inversion.

• Phonetics and Speech Sciences
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• v.8 no.3
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• pp.31-38
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• 2016

Speech inversion (acoustic-to-articulatory mapping) is not a trivial problem, despite the importance, due to the highly non-linear and non-unique nature. This study aimed to investigate the performance of Deep Neural Network (DNN) compared to that of traditional Artificial Neural Network (ANN) to address the problem. The Wisconsin X-ray Microbeam Database was employed and the acoustic signal and articulatory pellet information were the input and output in the models. Results showed that the performance of ANN deteriorated as the number of hidden layers increased. In contrast, DNN showed lower and more stable RMS even up to 10 deep hidden layers, suggesting that DNN is capable of learning acoustic-articulatory inversion mapping more efficiently than ANN.

• The Journal of the Acoustical Society of Korea
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• v.32 no.4
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• pp.308-316
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• 2013

A set of experiments (Shallow Water 2006, SW06) was carried out in shallow water near the New Jersey shelf break in summer 2006. Significant fluctuations in direct and surface reflected arrivals were observed from the chirp data (1100~2900 Hz) measured on a vertical line array. This paper presents a geoacoustic inverssion technique for short-range acoustic data with fluctuating arrivals and inversion results of experimental data. In order to reduce effects of random sea surface on the inversion, the acoustic energy back-propagated from the array to the source through direct and bottom-reflected paths is defined as the objective function. A multi-step inversion scheme is applied to the data using VFSR (Very Fast Simulated Reannealing) optimization technique. The inversion results show a source depth oscillation period equal to the measured ocean surface wave period. The inverted bottom sound speed is 1645 m/s and is similar to that estimated by other work at the same site.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.73-81
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• 2019

This paper proposes an acoustical inversion method using cepstrum analysis of underwater ship noise. Through the cepstrum analysis, multipath structure can be extracted from the recorded ship noise. The multipath structure comes from interferences between a direct arrival and multiple reflections from the sea surface and the bottom. The acoustic inversion is the optimization process to find the best parameters which show good correlation between cepstrums of the measured signal and the replica. The inversion method was applied to the underwater ship radiated noise data measured at Straits of Korea in order to estimate the acoustic center of the ship and the hydrophone position. The inversion results showed good agreement with the measured information.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.41 no.1
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• pp.54-60
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• 2008

High- and multi-frequency acoustic systems can measure a zooplankton patch successively and estimate the spatial distribution and abundance of zooplankton according to size using a multi-frequency inversion (MFI) method. This study measured zooplankton distribution to a depth of 150m using a multi-frequency acoustic system (TAPS-6), installed on a CTD system with a fluorometer and analyzed it using the MFI method. Simultaneously, zooplankton samples were collected by north pacific standard (NORPAC) net to confirm the species composition. The results showed that the combined method is valuable for estimating the zooplankton profile in detail and investigating the relationship between the zooplankton and phytoplankton profiles.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.202-209
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• 2019

In this study, an acoustic full-waveform inversion using Adam optimizer was proposed. The steepest descent method, which is commonly used for the optimization of seismic waveform inversion, is fast and easy to apply, but the inverse problem does not converge correctly. Various optimization methods suggested as alternative solutions require large calculation time though they were much more accurate than the steepest descent method. The Adam optimizer is widely used in deep learning for the optimization of learning model. It is considered as one of the most effective optimization method for diverse models. Thus, we proposed seismic full-waveform inversion algorithm using the Adam optimizer for fast and accurate convergence. To prove the performance of the suggested inversion algorithm, we compared the updated P-wave velocity model obtained using the Adam optimizer with the inversion results from the steepest descent method. As a result, we confirmed that the proposed algorithm can provide fast error convergence and precise inversion results.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1E
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• pp.31-37
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• 2001

Matched Field Processing (MFP) is a successive process of correcting mismatches between true and assumed parameters by matching the measured acoustic field data with numerically simulated data which we call replica. The MFP is widely used both in geo-acoustic parameter inversions and in source localizations. Whether a certain parameter can be inverted effectively or whether a source can be localized correctly depends on the amount of the influence that a parameter has on the acoustic field during the matching process. Sensitive parameters can be better estimated than the less sensitive ones in MFP. On the contrary, the sensitive parameters affect adversely on the source localization results when they have uncertainties. In this paper, a sensitivity index is defined based upon the field variation resulting from the perturbed parameters. Numerical test results show that the index behaves in accordance with the results of source localization under a mismatched environment and also with the inversion solutions.

• Geophysics and Geophysical Exploration
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• v.23 no.1
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• pp.38-49
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• 2020

Full-waveform inversion (FWI) is an optimization process of fitting observed and modeled data to reconstruct high-resolution subsurface physical models. In acoustic FWI (AFWI), pressure data acquired using a marine streamer has mainly been used to reconstruct the subsurface P-wave velocity models. With recent advances in marine seismic-acquisition techniques, acquiring multi-component data in marine environments have become increasingly common. Thus, AFWI strategies must be developed to effectively use marine multi-component data. Herein, we proposed an AFWI strategy using horizontal and vertical particle-acceleration data. By analyzing the modeled acoustic data and conducting sensitivity kernel analysis, we first investigated the characteristics of each data component using AFWI. Common-shot gathers show that direct, diving, and reflection waves appearing in the pressure data are separated in each component of the particle-acceleration data. Sensitivity kernel analyses show that the horizontal particle-acceleration wavefields typically contribute to the recovery of the long-wavelength structures in the shallow part of the model, and the vertical particle-acceleration wavefields are generally required to reconstruct long- and short-wavelength structures in the deep parts and over the whole area of a given model. Finally, we present a sequential-inversion strategy for using the particle-acceleration wavefields. We believe that this approach can be used to reconstruct a reasonable P-wave velocity model, even when the pressure data is not available.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.371-376
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• 2020

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.