Enhancing manual P -phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Mborah, Charles; Ge, Mao Chen

doi:10.1016/j.ijmst.2017.05.024

Cited by 30 publications

(10 citation statements)

References 25 publications

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“…This method was developed in 1971 [35,36] and is especially suitable for transient signals [27,28,29]. The AIC can efficiently separate different events into a temporary signal, or otherwise detect the arrival time of the signal [27,28,29]. The method is based on the following argument—on the left of the arrival time, the signal is basically noise, which is characterized by high entropy.…”

Section: Methodsmentioning

confidence: 99%

“…(2) Apply the Akaike information criterion as an alternative method to the classical threshold method, based on the entropy of the signals recorded by the piezoelectric sensors. This method, which has become very robust to the presence of noise in a signal and is especially suitable in a situation in which a smooth arrival of the wave occurs, has widely been used in other industrial sectors [27,28,29].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Location, Clone, and Measurement Season on the Propagation Velocity of Poplar Trees Using the Akaike Information Criterion for Arrival Time Determination

et al. 2019

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The purchase price of any forest plantation depends on the quality of its raw wood, and specifically, variables such as density, orientation of the fibers, bending strength, and bending MoE (Modulus of Elasticity). The elastic waves propagation velocity has become one of the most popular parameters to evaluate the wood in standing trees. This study had two objectives: (1) Show how this velocity is clearly affected by the clone, the location of the crop, and the measurement season of poplar crops; and (2) apply the Akaike information criterion to determinate the arrival time of the waves, on the basis of the entropy of the signals recorded by the piezoelectric sensors placed on the trunk of the tree.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Location, Clone, and Measurement Season on the Propagation Velocity of Poplar Trees Using the Akaike Information Criterion for Arrival Time Determination

et al. 2019

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“…into different frequency bands. Based on a pair of lowpass and high-pass conjugate quadrature filters h(j) and g(j) of the wavelet packet [27,28], D s or V s in each segment can be decomposed scale by scale into different frequency bands. In each frequency band, one corresponding decomposition coefficient is obtained, which is calculated as follows [29]:…”

Section: Analysis Methodmentioning

confidence: 99%

Evaluation of the Wind‐Resistant Performance of Long‐Span Cable‐Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

Gao-xin

Feng

2018

Shock and Vibration

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The wind-resistant performance of existing long-span cable-stayed bridge structures will inevitably deteriorate due to concrete carbonation, reinforcement corrosion, accumulated fatigue damage, etc., which can threaten the entire bridge safety. However, few studies currently focus on assessing the wind-resistant performance of existing long-span cable-stayed bridge structures, and at present, no studies have revealed how to identify the deterioration of the wind-resistant performance using a bridge health-monitoring technique. Therefore, based on the health-monitoring system installed on the Sutong Cable-stayed Bridge, the monitoring wind field and GPS displacement are captured to analyze the wind-resistant performance. First, the correlation between static cross wind and transversal displacement is analyzed, which is nearly linear but also contains discrete points caused by environmental noise. Second, considering that the discrete points can decrease the identification accuracy, one new method called the cross-correlation analysis of wavelet packet coefficients is put forward to effectively remove the discrete points. Third, considering that the traditional function cannot match the monitoring correlation very well, some new fitting functions are thoroughly studied to determine the best function for fitting the monitoring correlation. Fourth, the abnormal variation in the monitoring correlation caused by a deterioration in the wind-resistant performance is studied, and the root-mean-square (RMS) variable, which represents the difference between a good service state and a deteriorated service state, is used as a detection indicator to identify the deterioration of wind-resistant performance. Finally, the monitoring data from ten months are selected to evaluate the wind-resistant performance of the Sutong Bridge, and the result shows that its wind-resistant performance was still in a good service state during these ten months.

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“…In order to get a time-frequency spectrum, many signal processing methods have been advanced [19], including Wavelet Transform (WT) [20], Short Time Fourier Transform (STFT) [21], and Hilbert-Huang Transform (HHT) [22]. HHT is a superior algorithm among them, because this transformation does not follow the uncertainty principal, which enables acquiring a high resolution in both time domain and frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Determining Ultrasound Arrival Time by HHT and Kurtosis in Wind Speed Measurement

Liu

et al. 2021

Electronics

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The determination of ultrasonic echo signal onset time is the core of performing the time difference method to calculate wind speed. However, in practical cases, background noise makes precise determination extremely difficult. This paper carries out research on the accurate determination of onset time, exploring the advantages of an improved method based on the combination of Hilbert-Huang Transform (HHT) and high-order statistics (kurtosis). Performing Hilbert-Huang Transform to the received wave is aimed at determining a rough arrival time, around which a fixed size of data is extracted as initial sample to avoid a false pick. Then the fourth-order kurtosis of a smaller sample, extracted successively by a moving window from the initial sample, is calculated. The minimum point corresponds to the initial onset time. This approach was tested on a real ultrasonic echo signal dataset, acquired in a wind tunnel with an ultrasonic anemometer. The proposed method showed satisfying results in both ideal cases and low signal-to-noise ratio (SNR) environment, compared with traditional onset time determination approaches, including Akaike Information Criterion (AIC-picker), Short-term Average over Long-term Average (STA/LTA), and Teager-Kaiser energy operator (TKEO). The experimental results acquired by the HHT-kurtosis method demonstrated that the proposed method possesses a high accuracy.

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Enhancing manual P -phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Cited by 30 publications

References 25 publications

Effect of Location, Clone, and Measurement Season on the Propagation Velocity of Poplar Trees Using the Akaike Information Criterion for Arrival Time Determination

Effect of Location, Clone, and Measurement Season on the Propagation Velocity of Poplar Trees Using the Akaike Information Criterion for Arrival Time Determination

Evaluation of the Wind‐Resistant Performance of Long‐Span Cable‐Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

Determining Ultrasound Arrival Time by HHT and Kurtosis in Wind Speed Measurement

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