Signal reconstruction of surface waves on SASW measurement using Gaussian Derivative wavelet transform

2009 ◽  
Vol 57 (3) ◽  
pp. 616-635 ◽  
Author(s):  
Sri Atmaja P. Rosyidi ◽  
Mohd Raihan Taha ◽  
Zamri Chik ◽  
Amiruddin Ismail
Keyword(s):  
Wavelet Transform ◽  
Surface Waves ◽  
Signal Reconstruction ◽  
Gaussian Derivative

scholarly journals Fractional integration of seismic wavelets in anelastic media to recover multiscale properties of impedance discontinuities

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V61-V71 ◽  
Author(s):  
Stephan Ker ◽  
Yves Le Gonidec
Keyword(s):  
Wavelet Transform ◽  
Fractional Integration ◽  
Synthetic Data ◽  
Seismic Source ◽  
Seismic Attributes ◽  
Data Set ◽  
Shaping Filter ◽  
Gaussian Derivative ◽  
Intrinsic Attenuation ◽  
Seismic Wavelet

Multiscale seismic attributes based on wavelet transform properties have recently been introduced and successfully applied to identify the geometry of a complex seismic reflector in an elastic medium. We extend this quantitative approach to anelastic media where intrinsic attenuation modifies the seismic attributes and thus requires a specific processing to retrieve them properly. The method assumes an attenuation linearly dependent with the seismic wave frequency and a seismic source wavelet approximated with a Gaussian derivative function (GDF). We highlight a quasi-conservation of the Gaussian character of the wavelet during its propagation. We found that this shape can be accurately modeled by a GDF characterized by a fractional integration and a frequency shift of the seismic source, and we establish the relationship between these wavelet parameters and [Formula: see text]. Based on this seismic wavelet modeling, we design a time-varying shaping filter that enables making constant the shape of the wavelet allowing retrieval of the wavelet transform properties. Introduced with a homogeneous step-like reflector, the method is first applied on a thin-bed reflector and then on a more realistic synthetic data set based on an in situ acoustic impedance sequence and a high-resolution seismic source. The results clearly highlight the efficiency of the method in accurately restoring the multiscale seismic attributes of complex seismic reflectors in anelastic media by the use of broadband seismic sources.

scholarly journals LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaodan Liang ◽  
Zhaodi Ge ◽  
Liling Sun ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Wavelet Transform ◽  
Stock Price ◽  
Signal Reconstruction ◽  
Data Preprocessing ◽  
Original Data ◽  
Training Data ◽  
Threshold Method ◽  
Denoising Method ◽  
Stock Price Prediction ◽  
Price Prediction

For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.

scholarly journals Application of an Automatic Noise or Signal Removal Algorithm Based on Synchrosqueezed Continuous Wavelet Transform of Passive Surface Wave Imaging: A Case Study in Sichuan, China

2021 ◽  
Vol 11 (24) ◽  
pp. 11718
Author(s):  
Jie Fang ◽  
Guofeng Liu ◽  
Yu Liu
Keyword(s):  
Wavelet Transform ◽  
Surface Wave ◽  
Surface Waves ◽  
Continuous Wavelet Transform ◽  
Cross Correlation ◽  
Underground Structure ◽  
Inversion Method ◽  
Continuous Wavelet ◽  
Dispersion Measurement ◽  
Wave Imaging

Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.

Data Compression in Automatic Ultrasonic Inspection Based on Lifting Scheme

2011 ◽  
Vol 48-49 ◽  
pp. 353-356
Author(s):  
Xiong Bing Li ◽  
Hong Wei Hu ◽  
Ling Li ◽  
Lin Jin Tong
Keyword(s):  
Wavelet Transform ◽  
Data Compression ◽  
Signal Reconstruction ◽  
Ultrasonic Inspection ◽  
Signal Decomposition ◽  
Compression Method ◽  
High Compression Ratio ◽  
Algorithm Performance ◽  
Transform Coefficients ◽  
Optimizing Algorithm

In this paper, the method of data compression in ultrasonic automatic inspection using integral wavelet transform is proposed. The compression method presented is performed through signal decomposition, thresholding of wavelet transform coefficients, signal reconstruction, evaluating and optimizing algorithm performance by parameters index . The experiments show that the method has the advantages of low computation complexity， fast compression rate， high compression ratio and small reconstruction difference when it is applied to A-Wave data compression.

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