Method of automated extracting dispersion curves based on time-frequency distribution of seismic data
https://doi.org/10.18303/2619-1563-2023-3-4
Abstract
This paper discusses examples of testing a new implementation of the method of multi-channel surface wave analysis on synthetic data computed for elastic media with complex boundary geometry. The new implementation of the method includes developed algorithms for noise-resistant spectral analysis based on time-frequency domain filtering of seismograms and inversion of dispersion curves of phase velocities based on determination of ranges of possible transverse wave velocity models and application of artificial neural networks. Based on the results of synthetic data processing, the accuracy, lateral resolution limitations and applicability limits of the method under consideration are evaluated.
About the Authors
A. V. Yablokov
http://www.ipgg.sbras.ru/ru/institute/staff/yablokovav
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Koptyug Ave., 3, Novosibirsk, 630090
Novosibirsk State University
Pirogova Str., 1, Novosibirsk, 630090
Chinakal Institute of Mining SB RAS
Krasny Ave., 54, Novosibirsk, 630091
Russian Federation
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Koptyug Ave., 3, Novosibirsk, 630090
Novosibirsk State University
Pirogova Str., 1, Novosibirsk, 630090
Chinakal Institute of Mining SB RAS
Krasny Ave., 54, Novosibirsk, 630091
Russian Federation
A. S. Serdyukov
http://www.ipgg.sbras.ru/ru/institute/staff/serdyukovas
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Koptyug Ave., 3, Novosibirsk, 630090
Novosibirsk State University
Pirogova Str., 1, Novosibirsk, 630090
Chinakal Institute of Mining SB RAS
Krasny Ave., 54, Novosibirsk, 630091
Russian Federation
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Koptyug Ave., 3, Novosibirsk, 630090
Novosibirsk State University
Pirogova Str., 1, Novosibirsk, 630090
Chinakal Institute of Mining SB RAS
Krasny Ave., 54, Novosibirsk, 630091
Russian Federation
R. A. Efremov
http://www.ipgg.sbras.ru/ru/institute/staff/efremovra
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Koptyug Ave., 3, Novosibirsk, 630090
Novosibirsk State University
Pirogova Str., 1, Novosibirsk, 630090
Chinakal Institute of Mining SB RAS
Krasny Ave., 54, Novosibirsk, 630091
Russian Federation
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Koptyug Ave., 3, Novosibirsk, 630090
Novosibirsk State University
Pirogova Str., 1, Novosibirsk, 630090
Chinakal Institute of Mining SB RAS
Krasny Ave., 54, Novosibirsk, 630091
Russian Federation
References
1. Abdallatif T.F., Khozym A.A., Ghandour A.A. Determination of seismic site class and potential geologic hazards using multi-channel analysis of surface waves (MASW) at the Industrial City of Abu Dhabi, UAE // NRIAG Journal of Astronomy and Geophysics. – 2022. – Vol. 1. – P. 193–209, doi: 10.1080/20909977.2022.2055829.
2. Bohlen T., De Nil D., Köhn D., Jetschny S. SOFI2D seismic modeling with finite differences: 2D – elastic and viscoelastic version. User guide. – 2016.
3. Park C.B. MASW horizontal resolution in 2D shear-velocity (Vs) mapping // Open-File Report, Lawrence: Kansas Geologic Survey. – 2005. – Vol. 36. – 11 p.
4. Park C.B., Miller R.D., Xia J. Multichannel analysis of surface waves // Geophysics. – 1999. – Vol. 64 (3). – P. 800–808, doi: 10.1190/1.1444590.
5. Rahimi S., Wood C.M., Teague D.P. Performance of different transformation techniques for MASW data processing considering various site conditions, near-field effects, and modal separation // Surveys in Geophysics. – 2021. – Vol. 42. – P. 1197–1225, doi: 10.1007/s10712-021-09657-1.
6. Serdyukov A.S., Yablokov A.V., Duchkov A.A., Azarov A.A., Baranov V.D. Slant f-k transform of multichannel seismic surface wave data // Geophysics. – 2019. – Vol. 84 (1). – P. A19–A24, doi: 10.1190/geo2018-0430.1.
7. Stockwell R.G., Mansinha L., Lowe R.P. Localization of the complex spectrum: the S transform // IEEE transactions on signal processing. – 1996. – Vol. 44 (4). – P. 998–1001, doi: 10.1109/78.492555.
8. Yablokov A.V. Training algorithm an artificial neural network to inversion of the phase velocity of the surface wave // Proceedings of the XIX All-Russian Conference “Geodynamics. Geomechanics and Geophysics”, Novosibirsk, 22–28 July 2019. – 2019. – P. 156–157.
9. Yablokov A.V., Serdyukov A.S. Method of automated extracting of dispersion curves based on time-frequency distribution of seismic data // Russian Journal of Geophysical Technologies. – 2018. – Vol. 3. – P. 48–58, doi: 10.18303/2619-1563-2018-3-5.
10. Yablokov A.V., Serdyukov A.S., Loginov G.N., Baranov V.D. An artificial neural network approach for the inversion of surface wave dispersion curves // Geophysical Prospecting. – 2021. – Vol. 69 (7). – P. 1405–1432, doi: 10.1111/1365-2478.13107.
11. Yablokov A.V., Dergach P.A., Serdyukov A.S., Polozov S.S. Development and application of a portable vibroseis source for acquisition and analysis of seismic surface wave data // Seismic Instruments. – 2022. – Vol. 58 (S2). – P. S195–S203, doi: 10.3103/S074792392208014X.
12. Yablokov A.V., Serdyukov A.S. Yablokov A.V., Lugovtsova Y., Serdyukov A.S. Uncertainty quantification of multimodal surface wave inversion using artificial neural networks // Geophysics. – 2023. – Vol. 88 (2). – P. KS1–KS11, doi: 10.1190/geo2022-0261.1.
Review
For citations:
Yablokov A.V., Serdyukov A.S., Efremov R.A. Method of automated extracting dispersion curves based on time-frequency distribution of seismic data. Russian Journal of Geophysical Technologies. 2023;(3):4-16. (In Russ.) https://doi.org/10.18303/2619-1563-2023-3-4
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