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1

Time-reassigned multisynchrosqueezing of the S-transform for seismic time-frequency analysis

Liu Wei, Liu Yang, Li Shuangxi

Acta Geophysica

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2024

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Vol. 72, no. 2

759--775

EN

To accurately capture the time-frequency spectral anomaly, a novel time-frequency analysis (TFA) method, termed as time-reassigned multisynchrosqueezing S-transform (TMSSST), is proposed. In this study, we derive a N-order group delay (GD) estimator designed for frequency-domain S-transform to cope with the signal with fast varying instantaneous frequency (IF). By introducing an iterative reassignment procedure, the proposed TMSSST not only can produce a highly energy-concentrated time-frequency representation (TFR) but also can reconstruct the original signal with a high accuracy. Three synthetic signals are employed to validate the effectiveness of the proposed method by comparing with some classical TFA techniques such as S-transform (ST), synchrosqueezing S-transform (SSST) and time-reassigned synchrosqueezing S-transform (TSSST). It is shown that the TMSSST does a better job in addressing strongly frequency-varying signal. Application on field data further indicates the potential of highlighting subsurface geological structures and thus, facilitating seismic interpretation.

2

Adaptive time-reassigned synchrosqueezing transform for seismic random noise suppression

Liu Wei, Li Shuangxi, Chen Wei

Acta Geophysica

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2024

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Vol. 72, no. 2

829--847

EN

Noise suppression is of great importance to seismic data analysis, processing and interpretation. Random noise always overlaps seismic reflections throughout the time and frequency, thus, its removal from seismic records is a challenging issue. We propose an adaptive time-reassigned synchrosqueezing transform (ATSST) by introducing a time-varying window function to improve the time-frequency concentration, and integrate an improved Optshrink algorithm for the suppression of seismic random noise. First of all, a noisy seismic signal is transformed into a sparse time-frequency matrix via the ATSST. Then, the obtained time-frequency matrix is decomposed into a low-rank component and a sparse component via an improved Optshrink algorithm, where the D transformation and its first derivative are further simplified to reduce the computational burden of the original OptShrink algorithm. Finally, the denoised signal is reconstructed by implementing an inverse ATSST on the low-rank component. We have tested the proposed method using synthetic and real datasets, and make a comparison with some classical denoising algorithms such as f - x deconvolution and Cadzow filtering. The obtained results demonstrate the superiority of the proposed method in denoising seismic data.

3

Adptive heading control of underactuated unmanned surface vehicle based on improved backpropagation neural network

Dong Zaopeng, Li Jiakang, Liu Wei, Zhang Haisheng, Qi Shijie, Zhang Zhengqi

Polish Maritime Research

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2023

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nr 1

54--64

EN

Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused by the overlay and the interaction of multi interference, and also the uncertainties of model parameters, a heading control algorithm for an underactuated unmanned surface vehicle based on an improved backpropagation neural network is proposed. Based on applying optimization theory to realize that the underactuated unmanned surface vehicle tracks the desired yaw angle and maintains it, the improved momentum of weight is combined with an improved tracking differentiator to improve the robustness of the system and the dynamic property of the control. A hyperbolic tangent function is used to establish the nonlinear mappings an approximate method is adopted to summarize the general mathematical expressions, and the gradient descent method is applied to ensure the convergence. The simulation results show that the proposed algorithm has the advantages of strong robustness, strong anti-interference and high control accuracy. Compared with two commonly used heading control algorithms, the accuracy of the heading control in the complex environment of the proposed algorithm is improved by more than 50%.

4

Corporate environmental management with digital transformation : theory and applications

Liu Wei, Tsai Sang-Bing, Wu Chia-Huei, Shao Xuefeng, Wacławek Maria

Ecological Chemistry and Engineering. S

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2023

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Vol. 30, nr 2

155--157

5

Corporate environmental management in the context of digital transformation

Liu Wei, Wu Chia-Huei, Tsai Sang-Bing, Shao Xuefeng, Wacławek Maria

Ecological Chemistry and Engineering. S

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2023

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Vol. 30, nr 1

91--92

EN

Managers and the market place a higher importance on environmental management of businesses as sustainable development becomes the focus of attention. At the same time, the digital economy has become the most dynamic and emerging mode of economic development, driving future business trends and technological innovations. This special issue of Ecological Chemistry and Engineering S (ECE S) collects 6 articles focusing on the challenges and problems in the digital transformation and corporate environment management, which aims to share and discuss the recent advances and future trends of theory and application in academia, and to bring practical implications and experience in industry developers.

6

Corporate environmental management and sustainable operation : theory and application

Liu Wei, Tsai Sang-Bing, Wu Chia-Huei, Shao Xue-Feng, Wacławek Maria

Ecological Chemistry and Engineering. S

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2022

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Vol. 29, nr 3

283--285

7

Design and test study of a new mixed control method for magnetorheological semi-active suspension based on electromechanical analogy theory

Jiang Yu, Wang Ruochen, Ding Renkai, Sun Dong, Liu Wei

Journal of Theoretical and Applied Mechanics

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2021

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Vol. 59 nr 2

189--201

EN

For pursuing high performance, the development of semi-active suspension control tends to be complicated and ignores practicability. A new mixed control method effectively suppressing vibration of the vehicle body in the whole frequency band is proposed based on electromechanical analogy theory. Simulation results show that in comparison with passive suspension, on a long slope bumpy road, the mixed control reduces body acceleration by 21.49% and the maximum amplitude by 22.40%. On a C class road, the mixed control reduces body acceleration by 9.78%. Finally, an ECU hardware-in-the-loop test is conducted, which verifies the effectiveness and feasibility of the new mixed control method.

8

Green environment and sustainable development : methods and applications

Wu Chia-Huei, Tsai Sang-Bing, Liu Wei, Shao Xue-Feng, Xia Yang-Kun, Wacławek Maria

Ecological Chemistry and Engineering. S

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2021

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Vol. 28, nr 4

467--470

9

Eco-technology and eco-innovation for green sustainable growth

Wu Chia-Huei, Tsai Sang-Bing, Liu Wei, Shao Xue-Feng, Sun Rui, Wacławek Maria

Ecological Chemistry and Engineering. S

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2021

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Vol. 28, nr 1

7--10

EN

The world has developed to the point where productive activities are no longer only about profit and efficiency. With the rapid development of eco-technology across the world, the demand for eco-technology is growing rapidly and the question of how to protect the environment in production activities has been a popular topic for researchers in various countries. It was an inspiration for Professor Witold Wacławek (1938-2020) to found in 1992 a Central European Conference ECOpole and in 1994 the journal Ecological Chemistry and Engineering [1]. This Special Issue is dedicated to him.

10

Variable density acoustic RTM of VSP data based on the time–space domain LS-based SFD method

Liu Wei, You Jiachun, Cao Junxing, Liu Jianli, Hu Ruiqing

Acta Geophysica

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2021

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Vol. 69, no. 4

1269--1285

EN

Vertical seismic profling (VSP) can provide more abundant seismic wavefeld information and better seismic data with high resolution and high quality for the complex underground geological structures compared with surface seismic data. Reverse time migration (RTM) method possesses signifcant advantages for the accurate identifcation of complex geological structures, and it’s considered to be the most accurate imaging method at present. Therefore, we develop a variable density acoustic RTM method which is applicable for VSP data to enhance the recognition capability of complex geological structures, and we also discuss diferent aspects of this proposed imaging method. Firstly, to efectively improve the modeling precision of seismic wavefelds, the wavefeld extrapolation of our VSP RTM method is realized by using an optimal staggered-grid fnite diference (SFD) method to solve the variable density acoustic wave equation, because this optimal SFD method uses the least square (LS) method to optimize the objective function established by the time–space domain dispersion relation to estimate its diference coefcients. In other words, the time–space domain LS-based SFD method has higher numerical simulation accuracy for seismic modeling. Secondly, to efectively reduce the boundary refections and storage requirements of our VSP RTM method, we adopt the PML absorbing boundary and the efective boundary storage strategy in the process of wavefeld extrapolation. Finally, to strengthen the quality and precision of VSP RTM results, the depth imaging profle of a shot is calculated by the normalized cross-correlation imaging condition of sources which can efectively eliminate the source efects on RTM results, and Laplace fltering is applied to eliminate the imaging noises in fnal RTM results efectively. The imaging results of diferent models show the efectiveness of our RTM method for VSP data, and it can more accurately identify the complex underground geological structures compared with the RTM method for conventional surface seismic data.

11

Prestack AVO inversion for brittleness index of shale based on BI_Zoeppritz equation and NSGA II

Bi Chenchen, Wang Yanchun, Xie Wei, Sun Wei, Liu Wei

Acta Geophysica

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2020

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Vol. 68, no. 4

1067--1082

EN

As one of the evaluation characteristics of shale sweet spots, the brittleness index (BI) of shale formations is of great sig nifcance in predicting the range of sweet spots, and guiding hydraulic fracturing. Based on the three elastic parameters of P-wave velocity (VP), S-wave velocity (VS) and density obtained by conventional prestack AVO inversion, BI can be calcu lated indirectly using the Rickman formula. However, the conventional AVO inversion based on Zoeppritz approximation assumes that incident angle is small and elastic parameters change slowly, which afects the inversion accuracy of the three elastic parameters. Additionally, using these three elastic parameters to obtain BI indirectly also leads to cumulative errors of the inversion results. Therefore, we propose an inversion method based on BI_Zoeppritz equation to directly estimate VP, VS and BI. The BI_Zoeppritz equation is an exact Zoeppritz equation for BI, which is used as the forward operator for the proposed method. The multi-objective function of the inversion method is optimized by a fast nondominated sorting genetic algorithm (NSGA II). An initial model and an optimized search window are used to improve the inversion accuracy. The test results of model data and actual data reveal that this method can directly obtain the BI with high precision. In addition, the stability and noise immunity of the proposed method are verifed by the seismic data with random noise.

12

Random noise attenuation using a structure oriented adaptive singular value decomposition

Xu Yankai, Cao Siyuan, Pan Xiao, Liu Wei, Chen Hongling

Acta Geophysica

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2019

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Vol. 67, no. 4

1091--1106

EN

Singular value decomposition (SVD) is an efficient method to suppress random noise in seismic data. The performance of noise attenuation is typically affected by choosing the rank of the estimated signal using SVD. That the rank is fixed limits noise attenuation especially for a low signal-to-noise ratio data. Therefore, we propose a modified approach to attenuate random noise based on structure-oriented adaptively choosing singular values. In this approach, we first estimate dominant local slopes, predict other traces from a reference trace using the plane-wave prediction and construct a 3D seismic volume which is composed of all predicted traces. Then, we remove noise from a 2D profile whose traces are predicted from different reference traces via adaptive SVD filter (ASVD), which adaptively chooses the rank of estimated signal by the singular value increments. Finally, we stack every 2D denoised profile to a stacking denoised trace and reconstruct the 2D denoised seismic data which are composed of all stacking denoised traces. Synthetic data and field data examples demonstrate that the proposed structure-oriented ASVD approach performs well in random noise suppression for the low SNR seismic data with dipping and hyperbolic events.