Seismic Random Noise Attenuation via Noise Assisted-multivariate EMD Based MSSA

Ni, Weida; Lou, Yihuai; Xu, Xing; Shan, Zhigang; Xue, Shouzhong; Wang, Wei

doi:10.1109/icarce55724.2022.10046637

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…The NA-MEMD approach is used with the purpose of equating the signal quality received at various distances. This method was also used, to decompose the multidimensional signal generated by the vibration of the roller bearing [29], estimate the rate of change of frequency [30], oscillation detector [31], and filter noisy seismic data [32] From this research, the NA-MEMD approach was successful in breaking down multiple signals with frequencies of 15 kHz and 5 kHz, respectively, at the 4 and 10 meter distance variations indicated in Figures 6 and 7. As can be seen in Figures 8 and 9, the NA-MEMD is able to decompose signals with a wider range of frequencies, including 15 kHz, 5 kHz, and frequencies below 5 kHz, at distances of 20 and 50 meters.…”

Section: A Intensity Value Of Changes In Distancementioning

confidence: 95%

Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

Fajarwati,

Feriadi,

Widjiati

2023

Jurnal Teknik Elektro

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With current developments, underwater communication using acoustic signals is widely used. Many things need to be prepared to support a reliable underwater communication system, such as taking measurements in a test tank to find out the correct measurement configuration. Underwater acoustic intensity measurements, which are detailed in this paper, are performed in the test tank using distance variation schemes. Measurements were made at various distances of 4, 10, 20, and 50 meters from the signal source. The hydrophone that was used has a sensitivity of -180 dB re 1V/µPa. The hydrophone was placed at a depth of 2 meters below the surface of the water in the test tank, which divided the test tank depth in half to ensure that reflections from the bottom and the surface were kept to a minimum. However, the problem is that there are noisy signals at different frequencies. This paper proposes a method using Noise Assisted - Multivariate Empirical Mode Decomposition (NA-MEMD) to decompose the signal and then calculate the sound intensity. The result shows that an increase in the distance between the transmitter and receiver, also causes a change in the intensity with an average change of 0.467 dB/meter. It is concluded that the NA-MEMD approach was shown to be successful in decomposing the intended signal from the noise to equalize the quality of the signal received at different distances, and the correlation between intensity value and change in distance is resilient, with a correlation value of 0.98, indicating a very strong correlation.

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Section: A Intensity Value Of Changes In Distancementioning

confidence: 95%

Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

Fajarwati,

Feriadi,

Widjiati

2023

Jurnal Teknik Elektro

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“…In recent years, numerous signal processing methods have emerged for the separation and suppression of seismic noise (Yuan et al, 2012;Li et al, 2014;Zhang et al, 2021;Ni et al, 2022;Sun et al, 2022;. These methods include singular spectrum analysis (Oropeza and Sacchi, 2011), empirical mode decomposition-based techniques (Bekara and Baan, 2009), wavelet transform (Yang et al, 2018), and curvelet transform (Qu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing with log-sum heuristic recover for seismic denoising

Sun,

Zhang,

Wang

et al. 2024

Front. Earth Sci.

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The compressed sensing (CS) method, commonly utilized for restructuring sparse signals, has been extensively used to attenuate the random noise in seismic data. An important basis of CS-based methods is the sparsity of sparse coefficients. In this method, the sparse coefficient vector is acquired by minimizing the l1 norm as a substitute for the l0 norm. Many efforts have been made to minimize the lp norm (0 < p < 1) to obtain a more desirable sparse coefficient representation. Despite the improved performance that is achieved by minimizing the lp norm with 0 < p < 1, the related sparse coefficient vector is still suboptimal since the parameter p is greater than 0 rather than infinitely approaching 0 p→0+. Therefore, the CS method with the limit p→0+ is proposed to enhance the sparse performance and thus generate better denoised results in this paper. Our proposed method is referred to as the CS-LHR method because the solving process for minimizing p→0+ is the log-sum heuristic recovery (LHR). Furthermore, to improve the computational efficiency, we incorporate the majorization-minimization (MM) algorithm in this CS-LHR method. Experimental results of synthetic and real seismic records demonstrate the remarkable performance of CS-LHR in random noise suppression.

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Seismic Random Noise Attenuation via Noise Assisted-multivariate EMD Based MSSA

Cited by 2 publications

References 41 publications

Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

Underwater Acoustic Intensity Analysis using Noise Assisted-MEMD with Varying Distances

Compressed sensing with log-sum heuristic recover for seismic denoising

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