Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows

Pan, Shulin; Qin, Ziyu; Lan, Haiqiang; Badal, José

doi:10.1016/j.cageo.2018.12.001

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Based on this method, the single-trace energy ratio method (SMER) has achieved good application. The algorithm is described as follows (Pan et al 2019): where:…”

Section: Improved First-arrival Picking Methodsmentioning

confidence: 99%

“…Then, the "energy ratio method" was proposed (Coppens 1985), in which the first arrivals is determined by the energy ratio of the time windows corresponding to the current sample point. This algorithm has strong anti-noise ability and good automatic picking effect, also has achieved good results in actual data (Han et al 2009;Wong et al 2009;Pan et al 2019). In addition, many researchers have proposed other methods of first-arrival automatic picking, which are mainly based on neural network (Murat and Rudman 1992;McCormack et al 1993;Maity and Salehi 2016), the deep learning (Yuan et al 2018(Yuan et al , 2020Hu et al 2019;Tsai et al 2020), and fractal algorithm (Boschetti 1996;Jiao and Moon 2000;Chi-Durán et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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First-arrival automatic picking based on improved energy ratio method and outlier detection theory

Qin

Pan

et al. 2021

Acta Geophys.

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Based on the energy ratio method, an automatic picking method with strong noise resistance is proposed. It considers the influence of the current point's position on the first-arrival characteristic value. Specifically, an outlier detection technique is proposed to eliminate abnormal first arrivals for low signal-to-noise ratio (SNR) seismic data. First, the first arrivals of adjacent shots obtained by the new method are arranged according to the offsets. Then, combined with the distribution characteristics of the first arrivals, a symmetric window centered on the current point is established as the calculation range, and the distance-based outlier detection method is adopted for the abnormal first arrivals. The size of the calculation time window is determined by scanning the given value range. In order to optimize the processing results, we further propose an outlier detection method based on grid density. After this step, the abnormal first arrivals will be further eliminated. Following these steps, the abnormal first arrivals of all shots can be removed effectively. The actual data processing results show that the proposed program can accurately pick up the first arrivals and has a good performance in detecting the abnormal first arrivals. KeywordsFirst-arrival automatic picking • Abnormal first arrivals elimination • Distance-based outlier detection • Griddensity-based outlier detection Communicated by Prof. Sanyi Yuan (ASSOCIATE EDITOR), Prof. Michał Malinowski (CO-EDITOR-IN-CHIEF).

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“…Based on this method, the single-trace energy ratio method (SMER) has achieved good application. The algorithm is described as follows (Pan et al 2019): where:…”

Section: Improved First-arrival Picking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

First-arrival automatic picking based on improved energy ratio method and outlier detection theory

Qin

Pan

et al. 2021

Acta Geophys.

Self Cite

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“…These methods have certain advantages but suffer from a number of challenges when used on field data sets. Low energy levels and low SNR are the primary problems with these methods (Pan et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

First Break Traveltime Picking Using Unsupervised Machine Learning and Super‐Virtual Refraction Interferometry

Awais,

Hanafy,

Shafiq

et al. 2023

Earth and Space Science

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An accurate and trustworthy first break picking plays a key role in static correction calculations, velocity analysis, and deconvolution. First break traveltimes picking accuracy ensures correct and reliable seismic data processing results. Many methods are available to perform the first break pickings automatically. Most of these methods, however, are not robust when there is a low signal‐to‐noise ratio (SNR). In this study, to automatically determine the traveltimes of the first arrivals, we used the density‐based spatial clustering application with noise (DBSCAN) and the super‐virtual refraction interferometry (SVI). In the case of noisy data, using only DBSCAN for first break picking shows poor accuracy. To deal with this issue, SVI is employed to improve the SNR of the data, and then DBSCAN is applied again to the enhanced data for accurate picking. In the first step, DBSCAN is used to define a muting window for SVI. In the second step, SVI is employed to improve the first arrival SNR. In the third step, DBSCAN is applied to determine the final first arrival traveltime. The proposed approach is tested on synthetic and field data sets, where the manual and the cross‐correlation pickings are compared to the DBSCAN automatic pickings. We further test the proposed approach on a publicly available data set to benchmark our technique for first‐break accuracy with other established methods. Results show that the first arrival traveltime picks using the proposed approach are very accurate compared to manual picking.

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“…In recent years, image-processing techniques have also been applied in first-arrival picking. For example, Pan et al (2019) used the principle of image connectivity to filter the results of the energy ratio method. Correlation analysis can detect the similarity of two waveforms, and its process is not affected by random noise, which can be used to identify the first arrival (Gelchinsky & Shtivelman, 1983).…”

Section: Introductionmentioning

confidence: 99%

A first‐arrival wave recognition method based on the optimal dominant energy spectrum

Liu

et al. 2022

Geophysical Prospecting

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Refraction seismic data acquired by wide‐angle ocean‐bottom seismometers are typically used to invert geological structures. Accurate and reliable first‐arrival traveltime picks are therefore critical to successful refraction data processing. Unfortunately, signal‐to‐noise ratios of refraction data acquired by wide‐angle ocean‐bottom seismometers at far offsets are usually low; thus, conventional methods such as the energy ratio method and correlation analysis method usually fail to accurately and efficiently determine the refraction phase. We therefore develop a novel first‐arrival wave recognition method based on the optimal dominant energy spectrum to improve the accuracy of refraction wave picking on wide‐angle ocean‐bottom seismometer data. The method consists of three steps. The first step is to obtain the optimal dominant energy spectrum according to the power spectrum density of sample scanning. Second, the initial position of the first‐arrival wave is recognized by combining the range of the optimal dominant energy spectrum and the maximum point of its derivative. Third, the modified cross‐correlation method adjusts the initial position to obtain a more accurate arrival time. In this paper, we illustrate the workflow and feasibility of the proposed method via testing on model data. Then, the method is used to determine the first‐arrival time of field wide‐angle ocean‐bottom seismometer data acquired in the Southwest Pacific Ocean and Xisha Islands. The results show that our method can accurately pick the refraction phase at offsets as far as 80 km, thus significantly reducing the uncertainties in first‐arrival phase picking.

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Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows

Cited by 13 publications

References 18 publications

First-arrival automatic picking based on improved energy ratio method and outlier detection theory

First-arrival automatic picking based on improved energy ratio method and outlier detection theory

First Break Traveltime Picking Using Unsupervised Machine Learning and Super‐Virtual Refraction Interferometry

A first‐arrival wave recognition method based on the optimal dominant energy spectrum

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