Seismic $P$ Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain

Galiana-Merino, Juan José; Rosa-Herranz, Julio; Parolai, Stefano

doi:10.1109/tgrs.2008.2002647

Cited by 61 publications

(13 citation statements)

References 30 publications

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“…Another commonly applied method is the higher-order statistics-based Kurtosis/Skewness method [28]. In addition to the above methods, the damped predominant period (T pd ) method [29], multiband frequency analysis [30], and wavelet-based picking method [31] have also been developed for the absolute arrival time picking.…”

Section: B the Arrival Timementioning

confidence: 99%

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

2020

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Microseismic source location is a fundamental research interest in real-time microseismic monitoring and hazard risk assessment, and it provides the basis for determining the fracture zones and calculating seismic source parameters of the microseismicity (e.g., the event magnitude, the focal mechanism). In the present work, we carefully relocate some microseismic earthquakes that occurred in the Yongshaba mine (China) using the shooting 3-D ray-tracing (3D-RT) method based on a high-resolution 3-D velocity model, which is determined by a large amount of seismic data. Furthermore, a semiautomatic waveform cut method based on the cross-correlation (CC) technique (WCC) is developed for a quick, robust and precise determination of the direct P-phase relative delay times. Compared with the full waveform crosscorrelation (FWCC)-based method, the WCC-based method is free from the influence of complex later-phase waveform spectra. To verify the proposed method, we artificially generate the locations of 892 synthetic microseismic events, the P-phase travel times of which are calculated based on the given 3-D velocity model and the 3-D ray-tracing technique. The relocated hypocentres of these synthetic events obtained by the developed method are improved compared with those obtained by a homogeneous velocity model and the straight line-ray tracing based L1 norm time difference (TD) method (HV-SL-TD1), as well as those obtained by the 3-D velocity model and the straight line-ray tracing based L1 norm and L2 norm TD methods (3D-SL-TD1 and 3D-SL-TD2). Finally, we apply the proposed method to eight experimental blasts with pre-measured locations. The synthetic and application tests show that, despite some multi-path phenomena existing in the ray-tracing methods, the WCC-based method performs as well as the experienced manual picking method, and the results obtained by the 3D-RT location have smaller location errors (∼30 m) than those of the homogeneous velocity model-based methods (>40 m). The TD-based method is slightly better than the trigger time (TT)-based method when using the same norm, and the location precision of the L1 norm-based methods have a better resilience to larger picking errors than that of the L2 norm-based methods. Further improvements can be obtained by improving the resolution of the 3-D velocity model and including spatial voids in the model (e.g., tunnels and cavities) for the inversion.

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Section: B the Arrival Timementioning

confidence: 99%

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

2020

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“…Although traditional methods for event detection (based on the visual inspection of seismograms) obviously cannot be used, the most recently proposed methods, in particular those dealing with time-frequency analysis-based algorithms (e.g., Galiana-Merino et al, 2008, and the reference therein), might be not efficient/fast enough for real-time data analysis. Standard short-term average/long-term average (STA/LTA) algorithms (e.g., Allen, 1978) working in the time domain therefore are to be preferred, although they might be more susceptible to false event detection, especially in noisy urban environments (Küperkoch et al, 2012).…”

Section: Criteria For Event Detection Using a Single Stationmentioning

confidence: 99%

On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project

Parolai

Bindi

Boxberger

et al. 2015

Seismological Research Letters

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“…The modification is similar to the Discrete Stationary Wavelet Transform (Nason and Silverman, 1995;Galiana-Merino et al, 2008).…”

Section: The Wavelet Packet and Stationary Wavelet Packet Transformmentioning

confidence: 99%

Seismic wave characterization using complex trace analysis in the stationary wavelet packet domain

Galiana-Merino

Parolai

Rosa-Herranz

2011

Soil Dynamics and Earthquake Engineering

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One of the most important tasks in seismology and applied geophysics is the identification of the different kinds of waves that form a seismic record by means of polarization analysis. In particular, this involves the extraction of body waves (linear polarization) or surface waves (mostly elliptical polarization) from a set of seismic data and which forms a key point in several studies.In this work, a new method of time-frequency polarization analysis based on the stationary wavelet packet transform is developed. The proposed approach identifies and extracts automatically the different waves included in the signal, dependent upon the reciprocal ellipticity. Moreover, the algorithm provides enough information to the user to allow them to also manually select the reciprocal ellipticity intervals, and then extract the corresponding waves of interest contained in the signals.The proposed polarization estimation method and the automatic features extraction algorithm have been evaluated first using synthetic signals, and then applied to real seismic records. Based on the results obtained from both synthetic and real signals, we can conclude that the proposed method correctly identifies and extracts automatically the linearly and ellipticaly polarized waves from the signal, discerning clearly both types of polarization. Moreover, the proposed method is able to identify and extract signals with different kinds of elliptical polarization, allowing us to understand better the characteristics of Rayleigh waves.Index Terms -Polarization analysis, wave identification, seismic signal processing, stationary wavelet packet transform (SWPT).3

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Seismic $P$ Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain

Cited by 61 publications

References 30 publications

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project

Seismic wave characterization using complex trace analysis in the stationary wavelet packet domain

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