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Coal mine microseismic identification and frst-arrival picking based on Conv-LSTM-Unet

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Microseismic identification and first-arrival picking are the fundamental parts of microseismic monitoring. A deep learning model of convolution long short-term memory network (Conv-LSTM-Unet) was proposed to reduce the manual task of microseismic identification and first-arrival picking and overcome the problems of complex parameter design, low accuracy, and poor stability of traditional methods. This paper integrates microseismic identification and first-arrival picking tasks into a semantic segmentation task by using Conv-LSTM-Unet. In order to learn the spatio-temporal characteristics of microseismic, the model is based on the Unet network, and the Conv-LSTM model is formed by combining the convolution operation with LSTM, which replaces the convolution part of the original Unet network. Meanwhile, the microseismic signal is divided into three forms of single component, time–frequency map, and three-component signal to study the effect of microseismic input form on the identification model and first-arrival picking. The results show that when the signal input is three-component form, the model recognition and first-arrival picking effect are best. The Conv-LSTM-Unet model has outperformed other traditional models in first-arrival picking, with recognition accuracy up to 96.55% and maximum error of 4 ms.
Czasopismo
Rocznik
Strony
161--173
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
  • School of Safety Science and Engineering, Anhui University of Science and Technology, Tianjiaan District, Huainan 232000, China
  • Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Huainan 232001, China
autor
  • School of Safety Science and Engineering, Anhui University of Science and Technology, Tianjiaan District, Huainan 232000, China
  • Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Huainan 232001, China
  • Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Huainan 232001, China
  • School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Bibliografia
  • 1. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE T Pattern Anal 39:2481–2495
  • 2. Gaci S (2013) The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces. IEEE T Geosci Remote 52(8):4558–4563
  • 3. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
  • 4. Jiang P, Dai F, Xu N, Fan Y, Li B, Guo L, Xu J (2015) Identification of microseismic signal in underground powerhouse based on ST time-frequency analysis. Chin J Rock Mech Eng 34:4071–4079
  • 5. Kerfoot E, Clough J, Oksuz I, Lee J, King AP, Schnabel JA (2018) Left-ventricle quantification using residual U-Net. International Workshop on Statistical Atlases and Computational Models of the Heart pp: 371–380
  • 6. Kim Y (2014) Convolutional neural networks for sentence classification. Eprint Arxiv
  • 7. Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. Commun ACM 30(6):54–90
  • 8. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
  • 9. Lin BI, Wei X, Junjie Z, Hui Z (2018) Automatic classification of multi-channel microseismic waveform based on DCNN-SPP. J Appl Geophys 159:446–452
  • 10. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. IEEE T Pattern Anal pp:3131–3440
  • 11. Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2)
  • 12. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. Springer, Cham pp: 234–241
  • 13. Sabbione JI, Velis D (2010) Automatic first-breaks picking: New strategies and algorithms. Geophysics 75(4):V67–V76
  • 14. Shang X, Li X, Morales-Esteban A, Chen G (2017) Improving microseismic event and quarry blast classification using artificial neural networks based on principal component analysis. Soil Dyn Earthq Eng 99:142–149
  • 15. Shi X, Chen Z, Wang H, Yeung DY (2015) Convolutional LSTM network:a machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems. pp: 802–810
  • 16. Skoumal RJ, Brudzinski MR, Currie BS, Levy J (2014) Optimizing multi-station earthquake template matching through re-examination of the Youngstown, Ohio, sequence. Earth Planet Sc Lett 405:274–280
  • 17. Takanami T, Kitagawa G (1991) Estimation of the arrival times of seismic waves by multivariate time series model. Ann I Stat Math 43(3):407–433
  • 18. Vallejos JA, Mckinnon SD (2013) Logistic regression and neural network classification of seismic records. Int J Rock Mech Min 62:86–95
  • 19. Withers M, Aster R, Young C, Beiriger J, Harris M, Moore S, Trujillo J (1998) A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull Seismol Soc Am 88(1):95–106
  • 20. Yoon CE, O’Reilly O, Bergen KJ, Beroza GC (2015) Earthquake detection through computationally efficient similarity search. Sci Adv 1(11):e1501057
  • 21. Zhao YF, Wang JM, Pan YS, Wang XB, Zhao W, Fan Y (2020) Study on picking up microseismic P wave’s arrival based on quality optimization and normalized STA/LTA method. Chin J Rock Mech Eng 41:530–530
  • 22. Zheng J, Lu J, Peng S, Jiang T (2018) An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks. Geophys J Int 212(2):1389–1397
  • 23. Zhou S, Jiang H, Li J, Qu J, Zheng C, Li Y, Zhang Z, Guo Z (2021) Research on identification of seismic events based on deep learning: taking the records of ShanDong seismic network as an example. Seismol Geol 43(3):663–676
  • 24. Zhu Q, Jiang F, Yin Y, Yu Z, Wen J (2012) Classification of mine microseismic events based on wavelet-fractal method and pattern recognition. Chin J Rock Mech Eng 34:2036–2042
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fe621427-d841-4530-89ba-0a98258d211f
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