Iterative interferometry-based method for picking microseismic events

Iqbal, Naveed; Al-Shuhail, Abdullatif A.; Kaka, SanLinn I.; Liu, Entao; Raj, Anupama Govinda; McClellan, J.H.

doi:10.1016/j.jappgeo.2017.03.005

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…However, all these approaches require some parameters to adjust the noise filter threshold; inappropriate parameters will damage useful information or cause noise elimination failure. Seismic interferometry [22] has been introduced to increase the microseismic waveform signal-to-noise (S/N) ratio [23] by fixing the correct picking of a reference first break on at least one trace and then applying cross-correlation to provide picking convenience. Further, this concept has been transferred to the refraction field [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Iterative Interferometric Denoising Filter for Traveltime Picking

Qiao,

Zhou,

Hanafy

et al. 2024

Applied Sciences

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Traveltime picking accuracy is frequently affected by incoherent or random data noise. Within this context, we put forth a new denoising method called iterative interferometric denoising filtering. This method leverages the pseudo-Wigner distribution function to capture the offset and time-symmetric patterns of source wavelets convolved in seismic signals. Incoherent or random noises without this characteristic are eliminated via this approach. The processed data have waveform information distortion and more frequency components. However, the traveltime information can be considered correct, and the improved signal-to-noise ratio makes traveltime picking much more convenient. Our method’s practical applications in a synthetic and in two field datasets show that this technology can increase the signal-to-noise ratio, and the picked traveltime information can be used in traveltime tomography. These two field datasets were collected near the Aqaba Gulf and the Qademah fault, located in King Abdullah Economic City.

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Section: Introductionmentioning

confidence: 99%

Iterative Interferometric Denoising Filter for Traveltime Picking

Qiao,

Zhou,

Hanafy

et al. 2024

Applied Sciences

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“…Traditional manual picking often takes a lot of time and lowers the efficiency of the whole workflow. Therefore, many automatic picking methods were developed to accelerate this procedure (Fedorenko and Husebye, 1999; Sabbione and Velis, 2010; Mousa et al ., 2011; Blias, 2012; Mousa and Al‐Shuhail, 2012; Senkaya and Karsli, 2014; Velis et al ., 2015; Mousavi et al ., 2016; Tan and He, 2016; Iqbal et al ., 2017; Lee et al ., 2017; Khajouei and Goudarzi, 2018; Khalaf et al ., 2018; Mousavi et al ., 2019), leverage techniques relying on short‐term and long‐term average ratio (STA/LTA) (Allen, 1978; Jones and van der Baan, 2015; Vaezi and van der Baan, 2015) and attribute‐based methods (Saragiotis et al ., 2013; Forte et al ., 2016). Alvarez et al .…”

Section: Introductionmentioning

confidence: 99%

Iterative Gaussian mixture model and multi‐channel attributes for arrival picking in extremely noisy environments

Wang

Chen

2021

Geophysical Prospecting

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Arrival picking is a traditional and important topic in the field of seismic exploration. Depending on the quality of seismic data, the picking results are prone to large errors. As commonly used arrival‐picking strategies, conventional clustering methods, such as K‐means, suffer from insufficient flexibility, for example the clustering boundaries are fixed given a set of attribute vectors. In order to overcome this drawback, we propose a new strategy which can provide flexible clustering results (i.e. the variable clustering boundaries) based on an iterative Gaussian mixture model and utilize the local correlation among adjacent traces to enhance the anti‐noise ability. First, we use local principal component analysis to roughly distinguish the areas of signal waveforms. Then, two multi‐channel attributes are calculated and input to the iterative Gaussian mixture model. These two attributes can correctly identify the arrivals and avoid redundant computation caused by the high‐dimensional attribute vectors. The final step is to establish an optimized Gaussian mixture model and to iteratively select the proper boundaries for each trace. Although the iterative Gaussian mixture model takes a longer calculation time, the synthetic and real data tests have shown superior results over conventional methods even in situations of extremely low signal‐to‐noise ratios.

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“…Because the coal rock damage is mostly micronlevel vibration intensity, the signal is very weak, and the external noise causes the received signal to always be mixed with noise, which makes the signal's effectiveness greatly reduced [5]- [9]. In order to improve the effectiveness of coal and rock microseismic signals, it is necessary to make full use of noise suppression means to suppress the signal [10]- [14]. The noise suppression of the noisy microseismic signal can better provide real and effective signals to reduce the noise signal interference for the subsequent work such as the initial phase picking, the source location, and the focal mechanism interpretation.…”

Section: Introductionmentioning

confidence: 99%

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

et al. 2019

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Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can be used to perform better time-frequency localization analysis on signals containing a large amount of medium and high frequency information, according to the noise and useful signal components of both the characteristic of self-correlation function is different, the CEEMD and wavelet packet threshold jointed method is proposed. The method uses the energy concentration ratio to find noise and useful signal component demarcation point to denoise the microseismic signals. Firstly, we utilize adaptively decompose the signal from high frequency to low frequency by the CEEMD; Secondly, using the self-correlation method to select the intrinsic mode function (IMF) that needs noise suppression, the wavelet suppression method is used to suppress the noise of several high-frequency components whose self-correlation coefficient is below the critical value K; Finally, the IMF component after the wavelet packet threshold noise suppression is reconstructed with the noisefree IMF component. In order to verify the effectiveness of the proposed method on the noise suppression of microseismic signal, we added a Gaussian white noise to the Ricker wavelet signal similar to the microseismic signal. The experimental results show that the signal-to-noise ratio (SNR) of the signal is raised more than 10dB. The energy percentage is higher than 92%. In practical engineering, our proposal achieves an effective noise suppression effect on the microseismic signal.INDEX TERMS Complementary ensemble empirical mode decomposition, wavelet packet threshold, selfcorrelation, noise suppression.

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Iterative interferometry-based method for picking microseismic events

Cited by 14 publications

References 27 publications

Iterative Interferometric Denoising Filter for Traveltime Picking

Iterative Interferometric Denoising Filter for Traveltime Picking

Iterative Gaussian mixture model and multi‐channel attributes for arrival picking in extremely noisy environments

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

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