Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V211-V227 ◽  
Author(s):  
S. Mostafa Mousavi ◽  
Charles A. Langston
Keyword(s):  
Seismic Data ◽  
Ambient Noise ◽  
Cross Validation ◽  
Single Channel ◽  
Threshold Level ◽  
Noise Removal ◽  
Seismic Signals ◽  
Hard Thresholding ◽  
Resolution Time ◽  
Time Frequency

Recorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on synthetic, microseismic, and earthquake seismograms.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

scholarly journals Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1765
Author(s):  
Wei Xin ◽  
Fei Tian ◽  
Xiaocai Shan ◽  
Yongjian Zhou ◽  
Huazhong Rong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Machine Learning Algorithms ◽  
Seismic Exploration ◽  
Seismic Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
New Perspective ◽  
Geological Constraints ◽  
Frequency Features

As deep carbonate fracture-cavity paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.

Multichannel maximum-entropy method for the Wigner-Ville distribution

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. V25-V31
Author(s):  
Yanghua Wang ◽  
Ying Rao ◽  
Duo Xu
Keyword(s):  
Spectral Analysis ◽  
Maximum Entropy ◽  
Seismic Data ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Reflection Coefficients ◽  
Resolution Time ◽  
Maximum Entropy Spectral Analysis ◽  
Cross Term ◽  
Time Frequency

The Wigner-Ville distribution is a powerful technique for the time-frequency spectral analysis of nonstationary seismic data. However, the Wigner-Ville distribution suffers from cross-term interference between different wave components in seismic data. To mitigate cross-term interference, we have developed a multichannel maximum-entropy method (MEM) to modify the Wigner-Ville kernel. The method is related to the conventional maximum-entropy spectral analysis (MESA) algorithm because both algorithms use Burg’s reflection coefficients for the calculation of the prediction-error filter (PEF). The MESA algorithm works on the standard autocorrelation sequence, but it does not work for the Wigner-Ville kernel, which is an instantaneous autocorrelation sequence. Our multichannel MEM algorithm uses the PEF to modify any single Wigner-Ville kernel sequence by exploiting multiple Wigner-Ville kernel sequences simultaneously. This multichannel implementation is capable of robustly determining the reflection coefficient and a minimum-phased PEF for the Wigner-Ville kernel sequence. The Wigner-Ville distribution and the multichannel MEM algorithm in conjunction with each other in turn can produce a high-resolution time-frequency spectrum by mitigating the cross-term interferences and suppressing the spurious energy in the spectrum.

scholarly journals HVSR Analysis of Rockslide Seismic Signals to Assess the Subsoil Conditions and the Site Seismic Response

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Alessia Lotti ◽  
Veronica Pazzi ◽  
Gilberto Saccorotti ◽  
Andrea Fiaschi ◽  
Luca Matassoni ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Noise ◽  
Spectral Ratio ◽  
Effect Of Temperature ◽  
Precipitation Trend ◽  
Seismic Signals ◽  
Rock Falls ◽  
Time Frequency ◽  
Passive Seismic ◽  
On Line

Many Italian rock slopes are characterized by unstable rock masses that cause constant rock falls and rockslides. To effectively mitigate their catastrophic consequence thorough studies are required. Four velocimeters have been placed in the Torgiovannetto quarry area for an extensive seismic noise investigation. The study area (with an approximate surface of 200×100 m) is located near the town of Assisi (Italy) and is threatened by a rockslide. In this work, we present the results of the preliminary horizontal to vertical spectral ratio analysis of the acquired passive seismic data aimed at understanding the pattern of the seismic noise variation in case of stress state and/or weathering conditions (fluid content and microfracturing). The Torgiovannetto unstable slope has been monitored since 2003 by Alta Scuola of Perugia and the Department of Earth Sciences of the University of Firenze, after the observation of a first movement by the State Forestry Corps. The available data allowed an extensive comparison between seismic signals, displacement, and meteorological information. The measured displacements are well correlated with the precipitation trend, but unfortunately no resemblance with the seismic data was observed. However, a significant correlation between temperature data and the horizontal to vertical spectral ratio trend of the seismic noise could be identified. This can be related to the indirect effect of temperature on rock mass conditions and further extensive studies (also in the time frequency domain) are required to better comprehend this dependency. Finally, the continuous on-line data reveal interesting applications to provide near-real time warning systems for emerging potentially disastrous rockslides.

S-transform and Fourier transform frequency spectra of broadband seismic signals

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. O71-O81 ◽  
Author(s):  
Lin Wu ◽  
John Castagna
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
General Rule ◽  
Peak Frequency ◽  
Seismic Signals ◽  
Frequency Spectra ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform

The S-transform is one way to transform a 1D seismogram into a 2D time-frequency analysis. We have investigated its use to compute seismic interpretive attributes, such as peak frequency and bandwidth. The S-transform normalizes a frequency-dependent Gaussian window by a factor proportional to the absolute value of frequency. This normalization biases spectral amplitudes toward higher frequency. At a given time, the S-transform spectrum has similar characteristics to the Fourier spectrum of the derivative of the waveform. For narrowband signals, this has little impact on the peak frequency of the time-frequency analysis. However, for broadband seismic signals, such as a Ricker wavelet, the S-transform peak frequency is significantly higher than the Fourier peak frequency and can thus be misleading. Numerical comparisons of spectra from a variety of waveforms support the general rule that S-transform peak frequencies are equal to or greater than Fourier-transform peak frequencies. Comparisons on real seismic data suggest that this effect should be considered when interpreting S-transform spectral decompositions. One solution is to define the unscaled S-transform by removing the normalization factor. Tests comparing the unscaled S-transform with the S-transform and the short-windowed Fourier transform indicate that removing the scale factor improves the time-frequency analysis on reflection seismic data. This improvement is most relevant for quantitative applications.

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

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