Synchrosqueezing transform for geoacoustic inversion with air-gun source in the East China Sea

Liu, Hong; Yang, Kunde; Ma, Yuanliang; Yang, Qi; Huang, Chunlong

doi:10.1016/j.apacoust.2020.107460

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…Silva et al [40] proved that a synchrosqueezed CWT scalogram is more robust in rub detection than discrete Fourier transform (DFT) and fast Fourier transform (FFT). Liu et al [41] combined warping transform and synchronsqueezed transform to extract modal curves in shallow water waveguide. Synchronsqueezed transform was able to sharpen the frequency resolution, hence enabling more accurate characteristics to be identified.…”

Section: Reassigned and Synchrosqueezed Wavelet Transformmentioning

confidence: 99%

Efficacy Study of Fault Trending Algorithm to Prevent Fault Occurrence on Automatic Trampoline Webbing Machine

Shi

2022

Applied Sciences

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Nowadays, fault diagnostics is widely applied under Industry 4.0 to reduce machine maintenance costs, improve productivity, and increase machine availability. However, fault diagnostics are mostly post-mortem. When the fault is identified, it is already too late because damages have been done to the product and machine. This paper compares the efficacy of several signal data processing techniques for detecting faults that are about to occur. Our aim is to find an efficient way to predict the fault before it occurs. A continuous wavelet transform synchrosqueezed scalogram was found to be most suitable for this purpose, but it is difficult to apply. A novel procedure is proposed to count the number of pulses in the synchrosqueezed scalogram. A new method for detecting the trend from the pulse counts is then developed to predict the fault before it happens. The procedure and method are illustrated with experimental data collected while running an automated double-thread trampoline webbing machine.

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Section: Reassigned and Synchrosqueezed Wavelet Transformmentioning

confidence: 99%

Efficacy Study of Fault Trending Algorithm to Prevent Fault Occurrence on Automatic Trampoline Webbing Machine

Shi

2022

Applied Sciences

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“…The first WBS signal (marked as a blue point in Figure 2a) is used to find the MAP model as the initial model of the adaptive PF, also shown in Table 1. Because density is not the sensitive parameter in modal dispersion curves inversion [9,21], the densities of the sediment layers (ρ sed1 and ρ sed2 ) and bottom (ρ bot ) vary with the sound speed according to the Hamilton empirical function [22] c = 2330.4 − 1257.0ρ + 487.7ρ 2 . The corresponding estimated dispersion curves of the first WBS signal are shown in Figure 4d.…”

Section: Experiments Descriptionmentioning

confidence: 99%

“…The fact that only one hydrophone is needed to observe this phenomenon reduces the experimental cost and the complexity of recording systems [6,7]. Compared with inversion methods that require high signal-to-noise ration (SNR) and complex signal processing techniques, modal dispersion curves can be extracted from the environment with unknown parameters and strong noise by using warping transform [8], making this method especially suitable for wide-band signals (air-gun or explosive charges) received by one hydrophone [9].…”

Section: Introductionmentioning

confidence: 99%

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

Liu

Yang

2021

Remote Sensing

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An adaptive particle filter method is presented for performing sequential geoacoustic estimation of a shallow water acoustic environment using the explosive sound sources. This approach treats environmental parameters and source–receiver distance as unknown random variables that evolve as the source moves. As a sequential estimation method, this approach reduces the expense of computation than genetic algorithm and yields results with the same accuracy. Comparing with standard Particle filter, proposed method can adjust control parameters to adapt to a rapidly changing environment. This approach is demonstrated on the shallow water sound propagation data which was collected during the ASIAEX 2001 experiment. The results indicate that the geoacoustic parameters are well estimated and source–receiver distance are also well determined.

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“…In order to use the dispersion curves for geoacoustic parameter inversion, it is necessary to extract the dispersion curves for each mode from the spectrogram of the received signal, and then compare them with the simulated replicas obtained by the normal-mode-based propagation model. Recently, since warping transform was proposed as a good tool used for extracting the dispersion curves in the spectrogram (Bonnel et al, 2020), it has been applied to geoacoustic inversion studies in shallow water using various low-frequency broadband sound sources such as airgun, gunshot, light bulb, and whale call (Bonnel et al, 2013;Warner et al, 2015;Duan et al, 2016;Warner et al, 2016;Thode et al, 2017;Liu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Estimation of geoacoustic parameters and source range using airgun sounds in the East Siberian Sea, Arctic Ocean

Lee,

Han,

Choi

et al. 2024

Front. Mar. Sci.

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Dispersion is a representative property of low-frequency sound propagation over long distances in shallow-water waveguides, making dispersion curves valuable for geoacoustic inversion. This study focuses on estimating the geoacoustic parameters using the dispersion curves extracted from airgun sounds received in the East Siberian Sea. The seismic survey was conducted in September 2019 by the icebreaking research vessel R/V Araon, operated by the Korea Polar Research Institute. A single hydrophone was moored at the East Siberian Shelf, characterized by nearly range-independent shallow water (<70 m) with a hard bottom. In the spectrogram of the received sounds, the dispersion curves of the first two modes were clearly observed. Utilizing a combination of warping transform and wavelet synchrosqueezing transform these two modes were separated. Then, the geoacoustic parameters, such as sound speed and density in the sediment layer, were estimated by comparing the two modal curves extracted at a source-receiver distance of approximately 18.6 km with the predictions obtained by the KRAKEN normal-mode propagation model. Subsequently, the distances between the airgun and the receiver system in the 18.6 to 121.5 km range were estimated through the comparison between the measured modal curves and the model replicas predicted using the estimated geoacoustic parameters.

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Synchrosqueezing transform for geoacoustic inversion with air-gun source in the East China Sea

Cited by 13 publications

References 34 publications

Efficacy Study of Fault Trending Algorithm to Prevent Fault Occurrence on Automatic Trampoline Webbing Machine

Efficacy Study of Fault Trending Algorithm to Prevent Fault Occurrence on Automatic Trampoline Webbing Machine

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

Estimation of geoacoustic parameters and source range using airgun sounds in the East Siberian Sea, Arctic Ocean

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