Combination of Kolmogorov-Smirnov Statistic and Time-Frequency Representation for P-Wave Arrival Detection in Seismic Signal

2019 ◽  
pp. 166-174
Author(s):  
Jacek Wodecki ◽  
Anna Michalak ◽  
Paweł Stefaniak ◽  
Agnieszka Wyłomańska ◽  
Radosław Zimroz
Keyword(s):  
Seismic Signal ◽  
P Wave ◽  
Time Frequency ◽  
Frequency Representation ◽  
Kolmogorov Smirnov ◽  
Smirnov Statistic ◽  
Wave Arrival

scholarly journals Combination of Principal Component Analysis and Time-Frequency Representation for P-Wave Arrival Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Jacek Wodecki ◽  
Justyna Hebda-Sobkowicz ◽  
Adam Mirek ◽  
Radosław Zimroz ◽  
Agnieszka Wyłomańska
Keyword(s):  
Principal Component Analysis ◽  
High Efficiency ◽  
Onset Time ◽  
Principal Component ◽  
Component Analysis ◽  
P Wave ◽  
Time Frequency ◽  
Frequency Representation ◽  
Significant Difference ◽  
Wave Arrival

Seismic events are phenomena which commonly occur in the mining industry. Due to their dangerous character, such information as the energy of the potential event, the location of hazardous regions with higher seismic activity is considered valuable. However, the acquisition of this information is almost impossible without the ability to detect the onset time of the seismic event. The main objectives of algorithms in finding P-wave are high accuracy, reasonable time of operation, and automatic detection of wave arrival. In this paper, an innovative method which incorporates principal component analysis (PCA) with time-frequency representation of the signal is proposed. Due to the significant difference between the spectra of recorded seismic wave and pure noise which precedes the event, time-frequency representation allows for better accuracy of signal change detection. However, with an additional domain, the complexity rises. Thus, the incorporation of PCA (which is known for high efficiency in lowering data dimensions while maintaining original information) seems to be recommended. In order to show the feasibility of the method, it will be tested on real data originating from monitoring system used in underground mine.

P-Wave Onset Detection Based on the Spectrograms of the AE Signals

2011 ◽  
Vol 250-253 ◽  
pp. 3807-3810 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Acoustic Emissions ◽  
P Wave ◽  
Onset Detection ◽  
Arrival Times ◽  
Time Frequency ◽  
Frequency Representation ◽  
Reasonable Result ◽  
Adaptive Thresholds ◽  
Ae Signal ◽  
Ae Signals

Determining the onset of acoustic emissions (AE) signal is very time consuming if the onset is picked manually, so it is important to read the arrival times of P phase automatically. Automatic onset detection and picking algorithm has been proposed by applying the spectro-ratio on time–frequency sub-band. Powers of frequency sub-bands are determined by spectrogram as a time–frequency representation. Adaptive thresholds are calculated for one of these sub-bands to check if there is a P-wave arrival in the segment or not. To verify this check another test is done using the spectro-ratio. The application of this algorithm on the AE signals from the pencil-breaking test shows a reasonable result.

Seismic signal sparse time–frequency representation by Lp-quasinorm constraint

2019 ◽  
Vol 87 ◽  
pp. 43-59 ◽  
Author(s):  
Yingpin Chen ◽  
Zhenming Peng ◽  
Ali Gholami ◽  
Jingwen Yan ◽  
Shu Li
Keyword(s):  
Seismic Signal ◽  
Time Frequency ◽  
Frequency Representation

Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V117-V124 ◽  
Author(s):  
Mohammad Amir Nazari Siahsar ◽  
Saman Gholtashi ◽  
Amin Roshandel Kahoo ◽  
Hosein Marvi ◽  
Alireza Ahmadifard
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Real Data ◽  
Seismic Signal ◽  
Oil Field ◽  
Low Rank ◽  
Classical Formula ◽  
Sparse Decomposition ◽  
Time Frequency ◽  
Frequency Representation

Attenuation of random noise is a major concern in seismic data processing. This kind of noise is usually characterized by random oscillation in seismic data over the entire time and frequency. We introduced and evaluated a low-rank and sparse decomposition-based method for seismic random noise attenuation. The proposed method, which is a trace by trace algorithm, starts by transforming the seismic signal into a new sparse subspace using the synchrosqueezing transform. Then, the sparse time-frequency representation (TFR) matrix is decomposed into two parts: (a) a low-rank component and (b) a sparse component using bilateral random projection. Although seismic data are not exactly low-rank in the sparse TFR domain, they can be assumed as being of semi-low-rank or approximately low-rank type. Hence, we can recover the denoised seismic signal by minimizing the mixed [Formula: see text] norms’ objective function by considering the intrinsically semilow-rank property of the seismic data and sparsity feature of random noise in the sparse TFR domain. The proposed method was tested on synthetic and real data. In the synthetic case, the data were contaminated by random noise. Denoising was carried out by means of the [Formula: see text] classical singular spectrum analysis (SSA) and [Formula: see text] deconvolution method for comparison. The [Formula: see text] deconvolution and the classical [Formula: see text] SSA method failed to properly reduce the noise and to recover the desired signal. We have also tested the proposed method on a prestack real data set from an oil field in the southwest of Iran. Through synthetic and real tests, the proposed method is determined to be an effective, amplitude preserving, and robust tool that gives superior results over classical [Formula: see text] SSA as conventional algorithm for denoising seismic data.

Precise detection of a P‐wave in low S/N signal by using time‐frequency representations of a triaxial hodogram

Geophysics ◽  
1996 ◽  
Vol 61 (5) ◽  
pp. 1453-1466 ◽  
Author(s):  
Hirokazu Moriya ◽  
Hiroaki Niitsuma
Keyword(s):  
Signal Processing ◽  
Arrival Time ◽  
P Wave ◽  
Signal Processing Technique ◽  
Spectral Matrix ◽  
Arrival Times ◽  
Time Frequency ◽  
Microseismic Data ◽  
Test Of Hypotheses ◽  
Wave Arrival

We have developed a signal processing technique for three‐component microseismic data that allows the precise determination of P‐wave arrival times. The method is based on a time‐frequency representation of the signal that allows the evaluation of the 3-D particle motion from seismic waves in both time and frequency domains. A spectral matrix is constructed using the time‐frequency distributions. A crosscorrelation coefficient for the three‐component signal is derived through eigenvalue analysis of the spectral matrix. The P‐wave arrival time is determined through a statistical test of hypotheses using the crosscorrelation coefficient. This signal processing method is evaluated using a synthetic signal and it is compared to the local stationary autoregressive method for determining P‐wave arrival times. We also show that the proposed method is capable of determining the arrival time of a synthetic P‐wave to within 1 ms (five points in the discrete time series) in the presence of a signal‐to‐noise ratio of −5dB. The method can detect the arrival time of different frequency components of the P‐wave, which is a possibility for the evaluation of velocity dispersion of the seismic wave. We demonstrate the feasibility of the method further by applying it to microseismic data from a geothermal field.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

scholarly journals Method for designing multi-input system identification signals using a compact time-frequency representation

2021 ◽  
Author(s):  
Mathias Stefan Roeser ◽  
Nicolas Fezans
Keyword(s):  
System Identification ◽  
Design Method ◽  
Flight Test ◽  
Compact Representation ◽  
Time Frequency ◽  
Stability And Control ◽  
Frequency Representation ◽  
Aerodynamic Parameters ◽  
Definition Of ◽  
And Control

AbstractA flight test campaign for system identification is a costly and time-consuming task. Models derived from wind tunnel experiments and CFD calculations must be validated and/or updated with flight data to match the real aircraft stability and control characteristics. Classical maneuvers for system identification are mostly one-surface-at-a-time inputs and need to be performed several times at each flight condition. Various methods for defining very rich multi-axis maneuvers, for instance based on multisine/sum of sines signals, already exist. A new design method based on the wavelet transform allowing the definition of multi-axis inputs in the time-frequency domain has been developed. The compact representation chosen allows the user to define fairly complex maneuvers with very few parameters. This method is demonstrated using simulated flight test data from a high-quality Airbus A320 dynamic model. System identification is then performed with this data, and the results show that aerodynamic parameters can still be accurately estimated from these fairly simple multi-axis maneuvers.

scholarly journals Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3725
Author(s):  
Paweł Zimroz ◽  
Paweł Trybała ◽  
Adam Wróblewski ◽  
Mateusz Góralczyk ◽  
Jarosław Szrek ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Processing Technique ◽  
Underground Mine ◽  
Search And Rescue ◽  
Reference Spectrum ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Communication Device ◽  
Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

Sign in / Sign up

Export Citation Format

Share Document