Method for Identifying Micro-seismic P-Arrival by Time-frequency Analysis Using Intrinsic Time-Scale Decomposition

Zhang, Ruihong; Zhang, Lihua

doi:10.1515/acgeo-2015-0007

Cited by 7 publications

(2 citation statements)

References 13 publications

(12 reference statements)

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“…The time-frequency representation has been used for travel-time picking in the past (Herrera et al, 2015;Saragiotis et al, 2013;Zhang and Zhang, 2015). In this work, we have tested various time-frequency transforms but find spectrogram method to be most effective.…”

Section: Proposed Methodsmentioning

confidence: 99%

Iterative interferometry-based method for picking microseismic events

Iqbal

Al-Shuhail

Kaka

et al. 2017

Journal of Applied Geophysics

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Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.Comment: 31 pages, 18 Figure

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Section: Proposed Methodsmentioning

confidence: 99%

Iterative interferometry-based method for picking microseismic events

Iqbal

Al-Shuhail

Kaka

et al. 2017

Journal of Applied Geophysics

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“…The ratio of the short-term average over long-time average (STA/LTA) is the most commonly used algorithm for identifying arrival times [15]. This method is sensitive to background noise and the length of the window [16]. Some modified algorithms have been proposed, such as modified energy ratio, recursive STA/LTA, and characteristic function preprocessing [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

2018

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The arrival time of a microseismic event is an important piece of information for microseismic monitoring. The accuracy and efficiency of arrival time identification is affected by many factors, such as the low signal-to-noise ratio (SNR) of the records, the vast amount of real-time monitoring records, and the abnormal situations of monitoring equipment. In order to eliminate the interference of these factors, we propose a method based on phase-only correlation (POC) to estimate the relative arrival times of microseismic events. The proposed method includes three main steps:(1) The SNR of the records is improved via time-frequency transform, which is used to obtain the time-frequency representation of each trace of a microseismic event.(2) The POC functions of all pairs of time-frequency representations are calculated. The peak value of the POC function indicates the similarity of the traces, and the peak position in the time lag axis indicates the relative arrival times between the traces. (3) Using the peak values as weighting coefficients of the linear equations, consistency processing is used to exclude any abnormal situations and obtain the optimal relative arrival times. We used synthetic data and field data to validate the proposed method. Comparing with Akaike information criterion (AIC) and cross-correlation, the proposed method is more robust at estimating the relative arrival time and excluding the influence of abnormal situations.

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Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker

Shang

Weng

2017

J Seismol

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Method for Identifying Micro-seismic P-Arrival by Time-frequency Analysis Using Intrinsic Time-Scale Decomposition

Cited by 7 publications

References 13 publications

Iterative interferometry-based method for picking microseismic events

Iterative interferometry-based method for picking microseismic events

Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker

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