Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation

Laasri, El Hassan Ait; Akhouayri, Es-Saïd; Agliz, Dris; Atmani, Abderrahman

doi:10.1016/j.dsp.2013.12.009

Cited by 23 publications

(11 citation statements)

References 32 publications

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“…Each algorithm has a set of its parameters. Some of these parameters were set via optimization during the experiment [40]. To make the results more comparable, the parameters and all the values used for this study are given Table 6.…”

Section: Results and Comparison With Other Algorithmsmentioning

confidence: 99%

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Kırbaş

Peker

2016

Neural Comput & Applic

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Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival timing, determination of earthquake magnitude, and calculation of other parameters that can be used to make more accurate seismic maps are possible with the detection of these waves. Experts try to determine these waves by manual analysis. But this process is time-consuming and painful. In this study, a new method that enables the determination of P and S wave arrival times in noisy recordings is recommended. This method is based on the hybrid usage of empirical mode decomposition and Teager-Kaiser energy operator algorithms. The results show that the proposed system gives effective results in the automatic detection of P and S wave arrival times. Promisingly, the recommended system might serve as a novel and powerful candidate for the effective detection of P and S wave arrival time.

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Section: Results and Comparison With Other Algorithmsmentioning

confidence: 99%

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Kırbaş

Peker

2016

Neural Comput & Applic

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“…We assume that the noise average does not depend on time, that is, the seismic noise satisfies local stationary in short observation scale (Ait Laasri et al . ). Therefore, when all windows comprise only the noise part, then each of ER 1 and ER 2 (eq.…”

Section: Methodsmentioning

confidence: 97%

“…The seismic noise follows closely a Gaussian probability distribution (Ait Laasri et al . ), while at the opposite, the seismic signal is non‐Gaussian (Saragiotis et al . ; Persson ).…”

Section: Methodsmentioning

confidence: 99%

“…Beside the drawbacks concerning window size, threshold choice is another crucial problem of energy ratios methods (Akram and Eaton 2012). We assume that the noise average does not depend on time, that is, the seismic noise satisfies local stationary in short observation scale (Ait Laasri et al 2014). Therefore, when all windows comprise only the noise part, then each of ER 1 and ER 2 (eq.…”

Section: Multi-nested Windows Methodsmentioning

confidence: 99%

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Development of an adaptive multi‐method algorithm for automatic picking of first arrival times: application to near surface seismic data

Khalaf

Camerlynck

Florsch

et al. 2018

Near Surface Geophysics

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Accurate picking of first‐arrival times is important in many seismic studies, particularly in seismic tomography and reservoirs or aquifers monitoring. Many techniques have been developed, mainly for seismological purposes, in order to pick first arrivals automatically or semi‐automatically. However, these techniques do not reach the accuracy required in shallow seismics due to the complexity of near‐surface structures and low signal‐to‐noise ratio. We propose here a new adaptive algorithm to automatically pick first arrival in near‐surface seismic data by combining three picking methods: multi‐nested windows, higher order statistics, and Akaike information criterion. They benefit from combining different properties of the signal in order to highlight first arrivals and finally to provide an efficient and robust automatic picking. This strategy mimics the human first‐break picking, where a global trend is first defined at the beginning of the picking procedure. The exact first breaks are then sought in the vicinity of each point suggested by this trend. Three successive phases are combined in a multistage algorithm, each of them characterizing a specific signal property. Within each phase, the potential picks and their error range are automatically assessed and sequentially used as prior constraints in the following phase picking. Since having realistic estimates of the error in picked traveltimes is crucial for seismic tomography, our adaptive algorithm automatically provides picked arrival times with their associated uncertainties. We demonstrate the accuracy and robustness of the implemented algorithm using synthetic, pseudo‐synthetic and real datasets that pose challenges to classical automatic pickers. A comparison of both manual and adaptive picking procedures demonstrates that our new scheme provides more reliable results even under different noisy conditions. All parameters of our multi‐method algorithm are self‐adaptive, thanks to the sequential integration of each sub‐algorithm results in the workflow. Hence, it is nearly a parameter‐free algorithm, which is straightforward to implement and demands low computational resources.

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“…First, a statistical method is used to detect the statistical feature difference between the received data before and after first-arrival time. [11][12][13][14][15][16][17][18][19] However, it is non-robust against random noise. Second, a correlation method is used to find the time shift between interest data and reference data, then, the first-arrival time of interest data is calculated by both time shift and first-arrival time of reference data.…”

Section: Introductionmentioning

confidence: 99%

Novel automatic first-arrival picking method for ultrasound sound-speed tomography

Azuma

Imoto

et al. 2015

Jpn. J. Appl. Phys.

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Ultrasound sound-speed tomography (USST) is a promising technique for breast cancer diagnosis that is currently under investigation. Compared with two-dimensional X-ray mammography, it not only provides three-dimensional images but also avoids radiation exposure. However, the image quality of USST is highly dependent on the accuracy of travel time map (TTM). To improve the accuracy, a novel automatic first-arrival picking method is proposed in this study. With this method, Akaike information criterion is used to obtain travel time roughly, then cross-correlation of neighboring traces is employed to correct the obtained travel time. Simulation, phantom, and ex vivo experiments are implemented. The simulation experiments showed that the absolute errors of the proposed method were 52 and 98 ns for simple and complex structure data, respectively. The phantom and ex vivo experiments demonstrated the feasibility of the proposed method. In this study, a novel and robust first-arrival picking method was proposed for USST.

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Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation

Cited by 23 publications

References 32 publications

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Development of an adaptive multi‐method algorithm for automatic picking of first arrival times: application to near surface seismic data

Novel automatic first-arrival picking method for ultrasound sound-speed tomography

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