Abstract
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 follow α-stable distribution reveals that the microseismic signals have obvious pulse characteristics 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 microseismic 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.
Highlights
Microseismic monitoring technology has been widely applied to mine rock burst monitoring, oil and gas field fracturing monitoring, reservoir seismic monitoring, slope stability evaluation and so on
This paper intends to describe the characteristics of microseismic signals and noise with α-stable distributions, studies the impact of non-Gaussian noise on the spatial resolution of time difference of arrival (TDOA) and proposes an improved TDOA algorithm based on fractional low-order covariance (FLOC)
The spatial resolution on TDOA estimation of the generalized cross correlation (GCC), PHAT-GCC method based on the Gaussian distribution and the FLOC method based on the non-Gaussian distribution are compared and verified. α-stable distribution noises to the two Ricker wavelets are added
Summary
Microseismic monitoring technology has been widely applied to mine rock burst monitoring, oil and gas field fracturing monitoring, reservoir seismic monitoring, slope stability evaluation and so on. The α-stable distribution model has achieved excellence in the field of non-Gaussian signal processing, such as seismic inversion, speech de-noising and enhancement, sound source localization and mechanical fault diagnosis (Li and Yu, 2010; Yue et al, 2012; Zhang et al, 2014). This paper intends to describe the characteristics of microseismic signals and noise with α-stable distributions, studies the impact of non-Gaussian noise on the spatial resolution of TDOA and proposes an improved TDOA algorithm based on fractional low-order covariance (FLOC). Compared with the traditional TDOA algorithm, this improved algorithm could inhibit both the Gaussian noise and the α-stable distribution noise
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