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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.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1667--1677
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
autor
- Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China
autor
- School of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China
autor
- Southwest Geophysical Exploration Branch of Oriental Geophysical Company, CNPC, Chengdu 610213, China
autor
- Geophysical Research Institute, Shengli Oilfeld Company, SINOPEC, Dongying 257022, China
autor
- Petro China Southwest Oil & Gasfeld Company, Shale Gas Research Institute, Chengdu 610051, China
Bibliografia
- 1. An SP, Hu TY, Cui YF, Duan WS, Peng GX (2015) Auto-pick first-arrivals with complex raypaths for undulate surface conditions. Appl Geophys 12(01):93–100
- 2. Boschetti F (1996) A fractal-based algorithm for detecting first-arrivals on seismic traces. Geophysics 61(04):1095–1102
- 3. Breuning M, Kriegel H, Ng R, Sander J (2000) LOF: Identifying density-based local outliers. Int Conf Manag Data 29(02):93–104
- 4. Chi-Durán R, Comte D, Díaz M, Silva JF (2017) Automatic detection of P- and S-wave arrival times: new strategies based on the modified fractal method and basic matching pursuit. AGU Fall Meeting 21(5):1171–164. https://doi.org/10.1007/s10950-017-9658-0
- 5. Coppens F (1985) first-arrival picking on common-offset trace collections for automatic estimation of static corrections. Geophys Prospect 33(08):1212–1231
- 6. Gaci S (2014) The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces. IEEE Trans Geosci Remote Sens 52(08):4558–4563
- 7. Han L, Wong J, Bancroft JC (2009) Time picking and random noise reduction on microseismic data. CREWES Res Report 21(30):1–13
- 8. Hawkins D (l980) Identification of outliers. Chapman and Hall, London
- 9. Hu L, Zheng X, Duan Y, Yan X, Zhang X (2019) First-arrival picking with a u-net convolutional network. Geophysics 84(6):1–58
- 10. Jiao L, Moon WM (2000) Detection of seismic refraction signals using a variance fractal dimension technique. Geophysics 65(01):286–292
- 11. Knorr EM, Ng RT (1998) Algorithms for distance-based outliers in large databases. In: New York. Proc Vldb, pp 392–403
- 12. Maity D, Salehi I (2016) Neuro-evolutionary event detection technique for downhole microseismic surveys. Comp Geosci 86:23–33. https://doi.org/10.1016/j.cageo.2015.09.024
- 13. Marateb HR, Monica RM, Mansourian M, Merletti R, Villanueva MAM (2012) Outlier detection in high-density surface electromyographic signals 32nd Annual International Conference of the IEEE EMBS. Med Biolog Eng Comp 50(1):79–89. https://doi.org/10.1007/s11517-011-0790-7
- 14. McCormack MD, Zaucha DE, Dushek DW (1993) First-break refraction event picking and seismic data trace editing using neural networks. Geophysics 58(01):67–78
- 15. Mishra D, Soni D (2016) An integrated method for outlier detection with analytical study of distance based and angle based approaches. International Conference on Information & Communication Technology for Competitive Strategies, pp 1–5
- 16. Mousa W, Al-Shuhail A, Abdullatif A (2012) Enhancement of first-arrivals using the transform on energy-ratio seismic shot records. Geophysics 77(03):101–111
- 17. Murat M, Rudman A (1992) Automated first-arrivals picking: a neural network approach. Geophys Prospect 40(06):587–604
- 18. Pan SL, Qin ZY, Lan HQ, José B (2019) Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows. Comput Geosci 123:95–102
- 19. Peraldi R, Clement A (1972) Digital processing of refraction data: study of first-arrivals. Geophys Prospect 20(03):529–548
- 20. Sabbione JI, Velis D (2010) Automatic first-breaks picking: new strategies and algorithms. Geophysics 75(4):67–76
- 21. Tsai KC, Hu W, Wu X, Chen J, Han Z (2020) Automatic first-arrival picking via deep learning with human interactive learning. IEEE Trans Geosci Remote Sens 58(2):1380–1391
- 22. Wong J, Han L, Bancroft J, Stewart R (2009) Automatic time-picking of first-arrivals on noisy microseismic data. CSEG Microseismic Workshop, pp 1–6
- 23. Yuan S, Liu J, Wang S, Wang T, Shi P (2018) Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geosci Remote Sens Lett 15(2):1–5
- 24. Yuan P, Wang S, Hu W, Wu X, Nguyen HV (2020) A robust first-arrival picking workflow using convolutional and recurrent neural networks. Geophysics 85(5):1–44
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ca288f7d-4698-40e3-bd48-e76f72bb3b58