Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
The first-break detection for real seismic data with use of convolutional neural network. / Loginov, G.; Anton, D.; Litvichenko, D. et al.
81st EAGE Conference and Exhibition 2019. EAGE Publishing BV, 2019. p. 1-5 (81st EAGE Conference and Exhibition 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - The first-break detection for real seismic data with use of convolutional neural network
AU - Loginov, G.
AU - Anton, D.
AU - Litvichenko, D.
AU - Alyamkin, S.
N1 - Publisher Copyright: © 81st EAGE Conference and Exhibition 2019. All rights reserved.
PY - 2019/6/3
Y1 - 2019/6/3
N2 - In this study, we appraise a convolutional neural network for the detection of the first breaks on the real 3D seismic data set. The use of convolution as a learning kernel is followed by an assumption that the seismic trace can be considered as a convolution of source signal with the reflectivity function. The investigation area includes mixed elevations, floodplains of the rivers and the regions of strong permafrost, where the shingling effect is observed. We consider the first-break detection for each trace independently to preserve the complicated structure of the arrival times. The proposed approach was apprised on real exploration 3D seismic data set with size over 4.5 million traces. This test showed that the error between the original and predicted first breaks is not more than 3 samples for 95 percents of data set. The final quality control of picking results was established by the calculation of static corrections and computing seismic stacks, which showed that the proposed approach provides better results.
AB - In this study, we appraise a convolutional neural network for the detection of the first breaks on the real 3D seismic data set. The use of convolution as a learning kernel is followed by an assumption that the seismic trace can be considered as a convolution of source signal with the reflectivity function. The investigation area includes mixed elevations, floodplains of the rivers and the regions of strong permafrost, where the shingling effect is observed. We consider the first-break detection for each trace independently to preserve the complicated structure of the arrival times. The proposed approach was apprised on real exploration 3D seismic data set with size over 4.5 million traces. This test showed that the error between the original and predicted first breaks is not more than 3 samples for 95 percents of data set. The final quality control of picking results was established by the calculation of static corrections and computing seismic stacks, which showed that the proposed approach provides better results.
UR - http://www.scopus.com/inward/record.url?scp=85086690350&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.201901614
DO - 10.3997/2214-4609.201901614
M3 - Conference contribution
AN - SCOPUS:85086690350
T3 - 81st EAGE Conference and Exhibition 2019
SP - 1
EP - 5
BT - 81st EAGE Conference and Exhibition 2019
PB - EAGE Publishing BV
T2 - 81st EAGE Conference and Exhibition 2019
Y2 - 3 June 2019 through 6 June 2019
ER -
ID: 24567862