Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain

2022 ◽  
pp. 1-1
Author(s):  
Hamzeh Mohammadigheymasi ◽  
Paul Crocker ◽  
Maryam Fathi ◽  
Eduardo Almeida ◽  
Gracra Silveira ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Seismic Signals ◽  
Polarization Analysis ◽  
Time Frequency

Eigenvalue decomposition polarization analysis: A regularized sparsity-based approach

2021 ◽  
Author(s):  
Eduardo Almeida ◽  
Hamzeh Mohammadigheymasi ◽  
Maryam Fathi ◽  
Paul Crocker ◽  
Graça Silveira
Keyword(s):  
Frequency Domain ◽  
Decomposition Methods ◽  
The Body ◽  
Eigenvalue Decomposition ◽  
Seismic Signals ◽  
Polarization Analysis ◽  
Technical Program ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Dependent Polarization

<p>Polarization analysis is a signal processing tool for decomposing multi-component seismic signals to a set of rectilinearly or elliptically polarized elements. Theoretically, time-frequency polarization methods are the most compatible tool to analyze the intrinsically non-stationary seismic signals. They decompose the signal to a superposition of well-defined polarized elements, localized in the time and frequency domains. However, in practice, they suffer from instability and limited resolution for discriminating between interfering seismic phases in time and frequency, as the time-frequency decomposition methods are generally an underdetermined mapping from the time to the time-frequency domain. Our contribution is threefold: Firstly we obtain the frequency-dependent polarization properties in terms of the eigenvalue decomposition of the Fourier spectra of three-components of the signal. Secondly, by extending from the frequency to the time-frequency domain and using the regularized sparsity-based time-frequency decomposition (Portniaguine and Castagna, 2004) we are able to increase resolution and reduce instability in the presence of noise. Finally, by combining directivity, rectilineary, and amplitude attributes in the time-frequency domain, we extend the time-frequency polarization analysis to extract and filter different seismic phases. By applying this method on synthetic and real seismograms we demonstrate the efficacy of the method in discriminating between the interfering seismic phases in time and frequency, including the body, Rayleigh, Love, and coda waves. This research contributes to the FCT-funded SHAZAM (Ref. PTDC/CTA-GEO/31475/2017) project.<br><br><strong>REFERENCES</strong><br>Portniaguine, O., and J. Castagna, 2004, Inverse spectral decomposition, in SEG Technical Program Expanded Abstracts 2004: Society of Exploration Geophysicists, 1786–1789.</p>

scholarly journals Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhitao Gao ◽  
Song Zhang ◽  
Jianxian Cai ◽  
Li Hong ◽  
Jiangshan Zheng
Keyword(s):  
Noise Reduction ◽  
Frequency Domain ◽  
Seismic Signal ◽  
Signal Denoising ◽  
Seismic Signals ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Deep Network ◽  
Network Time ◽  
Feature Information

Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR). However, the current convolutional deep network used for seismic signal noise reduction does not make full use of the feature information extracted from all convolution layers in the network, and thus cannot fit the seismic signal with high SNR. To deal with this problem, this paper proposes the DnRDB model, a convolutional deep network time-frequency domain seismic signal denoising model combined with residual dense blocks (RDB). The model is mainly composed of several RDB in series. The input of each convolution layer in each RDB module is formed by the output of all the previous convolution layers. Meanwhile, even if the number of layers is increased, the fusion of the seismic signal features learned by the RDB modules can still achieve full extraction of seismic signals. Furthermore, deepening the model structure by concatenating multiple RDB modules enables further useful feature information to be extracted, which improves the SNR of seismic signals. The DnRDB model was trained and tested using the Stanford Global Seismic Dataset. The experimental results show that the DnRDB model can effectively recover seismic signals and remove various forms of noise. Even in the case of high noise, the denoised signal still has a high SNR. When the DnRDB model is compared with other denoising approaches such as wavelet threshold, empirical mode decomposition, and different deep learning methods, the results indicate that it performs best overall in denoising the same segment of the noisy seismic signal; the denoised signal also has less waveform distortion. Use of the DnRDB model in subsequent seismic signal processing work indicates that it can help the phase recognition algorithm improve the accuracy of seismic recognition through noise reduction.

scholarly journals Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain

2021 ◽  
Author(s):  
Hamzeh Mohammadigheymasi ◽  
Paul crocker ◽  
Maryam Fathi ◽  
Eduardo Almeida ◽  
Graça Silveira ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Seismic Waves ◽  
Eigenvalue Decomposition ◽  
Fourier Space ◽  
Polarization Analysis ◽  
Frequency Filtering ◽  
New Approach ◽  
Time Frequency ◽  
Frequency Representation ◽  
Dependent Polarization

In this paper, we present a new approach to the TF-domain PA methods. More precisely, we provide an in-detailed discussion on rearranging the eigenvalue decomposition polarization analysis (EDPA) formalism in the frequency domain to obtain the frequency-dependent polarization properties from the Fourier coefficients owing to the Fourier space orthogonality. Then, by extending the formulation to the TF-domain and incorporating sparsity-promoting time-frequency representation (SP-TFR), we alleviate the limited resolution when estimating the TFdomain polarization parameters. The final details of the technique are to apply an adaptive sparsity-promoting time-frequency filtering (SP-TFF) to extract and filter different phases of the seismic wave. By processing earthquake waveforms, we show that by combining amplitude, directivity, and rectilinearity attributes on the sparse TF-domain polarization map of the signal, we are able to extract or filter different phases of seismic waves.

scholarly journals Automatic traveltime picking using instantaneous traveltime

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. T53-T58 ◽  
Author(s):  
Christos Saragiotis ◽  
Tariq Alkhalifah ◽  
Sergey Fomel
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Field Data ◽  
Seismic Event ◽  
Seismic Signals ◽  
Recording Time ◽  
Time Frequency ◽  
Frequency Representation ◽  
Automatic Picking ◽  
The Time Domain

Event picking is used in many steps of seismic processing. We present an automatic event picking method that is based on a new attribute of seismic signals, instantaneous traveltime. The calculation of the instantaneous traveltime consists of two separate but interrelated stages. First, a trace is mapped onto the time-frequency domain. Then the time-frequency representation is mapped back onto the time domain by an appropriate operation. The computed instantaneous traveltime equals the recording time at those instances at which there is a seismic event, a feature that is used to pick the events. We analyzed the concept of the instantaneous traveltime and demonstrated the application of our automatic picking method on dynamite and Vibroseis field data.

scholarly journals Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain

2021 ◽  
Author(s):  
Hamzeh Mohammadigheymasi ◽  
Paul crocker ◽  
Maryam Fathi ◽  
Eduardo Almeida ◽  
Graça Silveira ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Seismic Waves ◽  
Eigenvalue Decomposition ◽  
Fourier Space ◽  
Polarization Analysis ◽  
Frequency Filtering ◽  
New Approach ◽  
Time Frequency ◽  
Frequency Representation ◽  
Dependent Polarization

In this paper, we present a new approach to the TF-domain PA methods. More precisely, we provide an in-detailed discussion on rearranging the eigenvalue decomposition polarization analysis (EDPA) formalism in the frequency domain to obtain the frequency-dependent polarization properties from the Fourier coefficients owing to the Fourier space orthogonality. Then, by extending the formulation to the TF-domain and incorporating sparsity-promoting time-frequency representation (SP-TFR), we alleviate the limited resolution when estimating the TFdomain polarization parameters. The final details of the technique are to apply an adaptive sparsity-promoting time-frequency filtering (SP-TFF) to extract and filter different phases of the seismic wave. By processing earthquake waveforms, we show that by combining amplitude, directivity, and rectilinearity attributes on the sparse TF-domain polarization map of the signal, we are able to extract or filter different phases of seismic waves.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

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