A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Mousa, Wail A.; Al-Shuhail, Abdullatif A.; Al-Lehyani, Ayman Fazea

doi:10.1190/geo2010-0322.1

Cited by 53 publications

(20 citation statements)

References 27 publications

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“…Thus, the initial time of the signal cluster is regarded as the first-arrival time. Others have reported many automatic picking schemes such as digital image segmentation [19], STA/LTA method [20,21], Akaike information criterion [22], fractal-based algorithm [23] and TDEE [11]. However, the detection accuracy of existing algorithms is still unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

2019

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First-arrival picking is a critical step in seismic data processing. This paper proposes the first-arrival picking through sliding windows and fuzzy c-means (FPSF) algorithm with two stages. The first stage detects a range using sliding windows on vertical and horizontal directions. The second stage obtains the first-arrival travel times from the range using fuzzy c-means coupled with particle swarm optimization. Results on both noisy and preprocessed field data show that the FPSF algorithm is more accurate than classical methods.

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Section: Introductionmentioning

confidence: 99%

First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

2019

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“…Coherence-based methods have been used to estimate the relative arrival time among traces. Artificial neural network, entropy function, and image processing method were used to pick arrivals automatically [29][30][31]. With the development of machine learning, some unsupervised and supervised algorithms were used to detect the arrival times of microseismic events [32,33].…”

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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“…There are other similar methods, e.g., multi-window method (Chen and Stewart, 2005) and modified energy ratio (MER) (Wong et al, 2009). Mousa et al (2011) discussed a method based on the digital image segmentation. Al-Shuhail et al (2013) proposed a workflow to enhance microseismic events and reported that the last step in their workflow (i.e., convolving the enhanced and raw records) seems responsible for leaking noise from the raw records into the enhanced data.…”

Section: Introductionmentioning

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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A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Cited by 53 publications

References 27 publications

First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

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

Iterative interferometry-based method for picking microseismic events

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