Cross-correlation analysis and time delay estimation of a homologous micro-seismic signal based on the Hilbert–Huang transform

Sun, Hong-Mei; Jia, Rui-Sheng; Du, Qianqian; Fu, You

doi:10.1016/j.cageo.2016.03.012

Cited by 43 publications

(14 citation statements)

References 15 publications

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“…Due to the existence of the pulse, the secondary moment of the observation data that follow the α-stable distribution is not convergent, and there is no limited high-order moment above the second order. However, the observation data that follow the Gaussian distribution have both stable secondary moment and limited high-order moment (Sun and Qiu, 2008). Therefore, whether or not the signal follows the Gaussian distribution can be de-termined from whether or not the sample variance of the observed data is convergent.…”

Section: Non-gaussian Property Of Microseismic Signalsmentioning

confidence: 99%

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

Jia¹,

Gong²,

Peng³

et al. 2017

Preprint

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Abstract. Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follows α-stable distribution, reveals that the pulse characteristics of the microseismic signal is outstanding and that the probability density curve of the microseismic signal is approximately symmetric. Thus, the hypothesis that microseismic signals follow the symmetric α-stable distribution is proposed. On the premise of this hypothesis, the characteristic exponent α of the seismic signals is obtained by utilizing the fractional low-order statistics, and then a new method of time difference of arrival (TDOA) estimation of microseismic signals based on fractional low-order covariance (FLOC) is proposed. Upon applying this method to the TDOA estimation of Ricker wavelet simulation signals and real microseismic signals, experimental results show that the FLOC method, which is based on the assumption of the symmetric α-stable distribution, leads to enhanced spatial resolution of the TDOA estimation relative to the generalized cross correlation (GCC) method, which is based on the assumption of the Gaussian distribution.

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Section: Non-gaussian Property Of Microseismic Signalsmentioning

confidence: 99%

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

Jia¹,

Gong²,

Peng³

et al. 2017

Preprint

Self Cite

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show abstract

“…Current microseismic source location methods mostly come from seismology. Now they are widely used in microseismic monitoring (Sun et al, 2016;Xue et al, 2015;Anikiev et al, 2014;Dong and Li, 2013). The earthquake source location method, based on time-difference principles, was proposed (Geiger, 1912).…”

Section: Introductionmentioning

confidence: 99%

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Sun

Zhang

Wang

et al. 2019

Nonlin. Processes Geophys.

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An intelligent method is presented for locating a microseismic source based on the particle swarm optimization (PSO) concept. It eliminates microseismic source locating errors caused by the inaccurate velocity model of the earth medium. The method uses, as the target of PSO, a global minimum of the sum of squared discrepancies between differences of modeled arrival times and differences of measured arrival times. The discrepancies are calculated for all pairs of detectors of a seismic monitoring system. Then, the adaptive PSO algorithm is applied to locate the microseismic source and obtain optimal value of the P-wave velocity. The PSO algorithm adjusts inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters to avoid the PSO algorithm trapping by local optima during the solution process. The origin time of the microseismic event is estimated by minimizing the sum of squared discrepancies between the modeled arrival times and the measured arrival times. This sum is calculated using the obtained estimates of the microseismic source coordinates and P-wave velocity. The effectiveness of the PSO algorithm was verified through inversion of a theoretical model and two analyses of actual data from mine blasts in different locations. Compared with the classic least squares method (LSM), the PSO algorithm displays faster convergence and higher accuracy of microseismic source location. Moreover, there is no need to measure the microseismic wave velocity in advance: the PSO algorithm eliminates the adverse effects caused by error in the P-wave velocity when locating a microseismic source using traditional methods.

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“…Current microseismic source location methods mostly come from earth quake seismology. Now they are widely used in microseismic monitoring (Sun et al, 2016;Xue et al, 2015;Anikiev et al, 2014;Dong and Li, 2013).The earthquake source location method, based on time-difference principles was proposed (Geiger, 1912). Based on this work, Lienert et al…”

Section: Introductionmentioning

confidence: 99%

Technique for solving for microseismic source location parameters based on adaptive particle swarm optimization

Sun

Zhang

Bing-guo

et al. 2019

Preprint

Self Cite

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Abstract. An intelligent method is presented for locating microseismic source based on particle swarm optimization (PSO) concept. It eliminates microseismic source locating errors caused by inaccurate velocity model of the earth medium. The method uses as the target of PSO a global minimum of the sum of squared discrepancies between modeled arrival times and measured arrival times. The discrepancies are calculated for all pairs of detectors of a seismic monitoring system, Then, the adaptive PSO algorithm is applied to locate the microseismic source and obtain optimal value of the P-wave velocity. The PSO algorithm adjusts inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters to avoid the PSO algorithm trapping by local optima during the solution process. The origin time of the microseismic event is estimated by minimizing the sum of squared discrepancies between the modeled arrival times and the measured arrival times. This Sum is calculated using the obtained estimates of the microseismic source coordinates and P-wave velocity. The effectiveness of the PSO algorithm was verified through inversion of a theoretical model and two analyses of actual data from mine blasts in different locations. Compared with the classic least squares method, the PSO algorithm displays faster convergence and higher accuracy of microseismic source positioning. Moreover, there is no need to measure the microseismic wave velocity in advance: the PSO algorithm eliminates the adverse effects caused by error in the P-wave velocity when locating a microseismic source using traditional methods.

show abstract

Cross-correlation analysis and time delay estimation of a homologous micro-seismic signal based on the Hilbert–Huang transform

Cited by 43 publications

References 15 publications

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Technique for solving for microseismic source location parameters based on adaptive particle swarm optimization

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