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Detection of clock errors in seismic records and estimation of time shifts for a seismic network

https://doi.org/10.18303/2619-1563-2022-1-134

Abstract

In the records of autonomous seismological observations occurrence of quartz watch clock drift often leads to incorrect time records. This paper presents a method for detecting such errors in a seismic station network based on the analysis of ambient seismic noise cross-correlation functions using Monte-Carlo Markov chain (MCMC) approach without using a reference signal. The proposed method was tested on the seismic data of a temporary seismic network installed on Paramushir Island in 2021-2022 for which time shifts were successfully estimated and corrections to the cross-correlation functions were made.

About the Authors

N. N. Belovezhets
http://www.ipgg.sbras.ru/ru/institute/staff/belovezhetsnn
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS Novosibirsk State University
Russian Federation

Koptyug Ave., 3, Novosibirsk, 630090



Y. M. Berezhnev
http://www.ipgg.sbras.ru/ru/institute/staff/berezhnevym
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS Novosibirsk State University
Russian Federation

Koptyug Ave., 3, Novosibirsk, 630090



A. V. Jakovlev
http://www.ipgg.sbras.ru/ru/institute/staff/jakovlevav
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS
Russian Federation

Koptyug Ave., 3, Novosibirsk, 630090



S. S. Abramenkov
http://www.ipgg.sbras.ru/ru/institute/staff/abramenkovss
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS Novosibirsk State University
Russian Federation

Koptyug Ave., 3, Novosibirsk, 630090



I. F. Abkadyrov
http://www.ipgg.sbras.ru/ru/institute/staff/abkadyrovif
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS Institute of Volcanology and Seismology FB RAS
Russian Federation

Koptyug Ave., 3, Novosibirsk, 630090



References

1. Bensen G.D., Ritzwoller M.H., Barmin M.P., Levshin A.L., Lin F., Moschetti M.P., Shapiro N.M., Yang Y. Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements // Geophysical Journal International. – 2007. – Vol. 169 (3). – P. 1239–1260, doi: 10.1111/j.1365-246X.2007.03374.x.

2. Ermert L., Sager K., Afanasiev M., Boehm C., Fichtner A. Ambient seismic source inversion in a heterogeneous Earth: theory and application to the Earth’s hum // Journal of Geophysical Research: Solid Earth. – 2017. – Vol. 122 (11). – P. 9184–9207, doi: 10.1002/2017JB014738.

3. Hable S., Sigloch K., Barruol G., Stahler S.C., Hadziioannou C. Clock errors in land and ocean bottom seismograms: high-accuracy estimates from multiple-component noise cross-correlations // Geophysical Journal International. – 2018. – Vol. 214. – P. 2014–2034, doi: 10.1093/gji/ggy236.

4. Hastings W.K. Monte Carlo sampling methods using Markov Chains and their applications // Biometrika. – 1970. – Vol. 57 (1). – P. 97–109, doi: 10.1093/biomet/57.1.97.

5. Larose E., Derode A., Campillo M., Fink M. Imaging from one-bit correlations of wideband diffuse wave fields // Journal of Applied Physics. – 2004. – Vol. 95 (12). – P. 8393–8399, doi: 10.1063/1.1739529.

6. Moreau L., Stehly L., Boué P., Lu Y., Larose E., Campillo M. Improving ambient noise correlation functions with an SVD-based Wiener filter // Geophysical Journal International. – 2017. – Vol. 211. – P. 418–426, doi: 10.1093/gji/ggx306.

7. Paitz P., Sager K., Fichtner A. Rotation and strain ambient noise interferometry // Geophysical Journal International. – 2019. – Vol. 216. – P. 1938–1952, doi: 10.1093/gji/ggy528.

8. Sabra K.G., Gerstoft P., Roux P., Kuperman W.A. Surface wave tomography from microseisms in Southern California // Geophysical Research Letters. – 2005. – Vol. 32 (14). – P. L14311, doi: 10.1029/2005GL023155.

9. Sens-Schönfelder C. Synchronizing seismic networks with ambient noise // Geophysical Journal International. – 2008. – Vol. 174 (3). – P. 966–970, doi: 10.1111/j.1365-246X.2008.03842.x.

10. Shapiro N.M., Campillo M., Stehly L., Ritzwoller M.H. High-resolution surface-wave tomography from ambient seismic noise // Science. – 2005. – Vol. 307 (5715). – P. 1615–1618, doi: 10.1126/science.110833.

11. Snieder R. Extracting the Green’s function from the correlation of coda waves: A derivation based on stationary phase // Physical Review E. – 2004. – Vol. 69. – P. 046610, doi: 10.1103/PhysRevE.69.046610.

12. Stehly L., Campillo M., Shapiro N.M. Traveltime measurements from noise correlation: stability and detection of instrumental time-shifts // Geophysical Journal International. – 2007. – Vol. 171 (1). – P. 223–230, 10.1111/j.1365-246X.2007.03492.x.

13. Tarantola A. Inverse problem theory and methods for model parameter estimation. – SIAM, Philadelphia, 2005. – 333 p.

14. Taylor G., Hillers G. Estimating temporal changes in seismic velocity using a Markov chain Monte Carlo approach // Geophysical Journal International. – 2020. – Vol. 220 (3). – P. 1791–1803, doi: 10.1093/gji/ggz535.

15. Wapenaar K. Retrieving the elastodynamic Green’s function of an arbitrary inhomogeneous medium by cross correlation // Physical Review Letters. – 2004. – Vol. 93. – P. 254301, doi: 10.1103/PhysRevLett.93.254301.


Review

For citations:


Belovezhets N.N., Berezhnev Y.M., Jakovlev A.V., Abramenkov S.S., Abkadyrov I.F. Detection of clock errors in seismic records and estimation of time shifts for a seismic network. Russian Journal of Geophysical Technologies. 2022;(1):134-142. (In Russ.) https://doi.org/10.18303/2619-1563-2022-1-134

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