Singular Points of the Tremor of the Earth’s Surface
Source abstract
Abstract
A method for studying properties of the Earth’s surface tremor, measured by means of GPS, is proposed. The following tremor characteristics are considered: the entropy of wavelet coefficients, the Donoho–Johnston wavelet index, and two estimates of the spectral slope. The anomalous areas of tremor are determined by estimating the probability densities of extreme values of the studied properties. The criteria for abnormal tremor behavior are based on the proximity to, or the difference between, tremor properties and white noise. The greatest deviation from the properties of white noise is characterized by entropy minima and spectral slope and DJ index maxima. This behavior of the tremor is called “active”. The “passive” tremor behavior is characterized by the maximum proximity to the properties of white noise. The principal components approach provides weighted averaged density maps of these two variants of extreme distributions of parameters in a moving time window of 3 years. Singular points are the points of maximum average densities. The method is applied to the analysis of daily time series from a GPS network in California during the period 2009–2022. Singular points of tremor form well-defined clusters were found. The passive tremor could be caused by the activation of movement in fragments of the San Andreas fault.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Earthquake Detection and Analysis · Statistical and numerical algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Physics of the Earth, Russian Academy of Sciences, 123242 Moscow, Russia
Funding
Institute of Physics of the Earth of the Russian Academy of Sciences
References (29)
Show source-provided references
1. Maximum-likelihood attitude estimation using GPS signals10.1016/j.dsp.2006.09.001 ↗
2. Wang, F., Li, H., and Lu, M. (2017). GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation. Sensors, 17.10.3390/s17071532 ↗
3. Parkinson, B.W. (1996). Global Positioning System: Theory and Applications, AIAA.10.2514/4.866395 ↗
4. A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks10.1155/2016/2080536 ↗
5. The time domain behavior of power law noises10.1029/91GL02832 ↗
6. Assessment of noise in GPS coordinate time series: Methodology and results
7. Fast error analysis of continuous GPS observations10.1007/s00190-007-0165-x ↗
8. On modelling GPS phase correlations: A parametric model10.1007/s40328-017-0209-5 ↗
9. Liu, B., Xing, X., Tan, J., and Xia, Q. (2020). Modeling Seasonal Variations in Vertical GPS Coordinate Time Series Using Independent Component Analysis and Varying Coefficient Regression. Sensors, 20.10.3390/s20195627 ↗
10. Vertical deformations from homogeneously processed GRACE and global GPS long-term series10.1007/s00190-010-0437-8 ↗
11. Separation of Sources of Seasonal Uplift in China Using Independent Component Analysis of GNSS Time Series10.1029/2019JB018139 ↗
12. The Quasi-Biennial Vertical Oscillations at Global GPS Stations: Identification by Ensemble Empirical Mode Decomposition10.3390/s151026096 ↗
13. Extracting seasonal deformations of the Nepal Himalaya region from vertical GPS position time series using Independent Component Analysis10.1016/j.asr.2017.02.028 ↗
14. Geodetic secular velocity errors due to interannual surface loading deformation10.1093/gji/ggv190 ↗
15. Horizontal motion in elastic response to seasonal loading of rain water in the Amazon Basin and monsoon water in Southeast Asia observed by GPS and inferred from GRACE10.1002/2013GL058093 ↗
16. Modeling deformation induced by seasonal variations of continental water in the Himalaya region: Sensitivity to Earth elastic structure10.1002/2013JB010451 ↗
17. Review of current GPS methodologies for producing accurate time series and their error sources10.1016/j.jog.2017.01.004 ↗
18. Anomalous harmonics in the spectra of GPS position estimates10.1007/s10291-007-0067-7 ↗
19. Global coherence of GPS-measured high-frequency surface tremor motions10.1007/s10291-018-0781-3 ↗
20. Field of coherence of GPS-measured earth tremors10.1007/s10291-019-0909-0 ↗
21. Lyubushin, A. (2022). Identification of Areas of Anomalous Tremor of the Earth’s Surface on the Japanese Islands According to GPS Data. Appl. Sci., 12.10.3390/app12147297 ↗
22. Harnessing the GPS data explosion for interdisciplinary science10.1029/2018EO104623 ↗
23. Duda, R.O., Hart, P.E., and Stork, D.G. (2000). Pattern Classification, Wiley.
24. Marple, S.L. (1987). Digital Spectral Analysis with Applications, Prentice-Hall, Inc.
25. Mallat, S. (1999). A Wavelet Tour of Signal Processing, Academic Press. [2nd ed.].10.1016/B978-012466606-1/50008-8 ↗
26. Adapting to unknown smoothness via wavelet shrinkage10.1080/01621459.1995.10476626 ↗
27. Lyubushin, A. (2021). Low-Frequency Seismic Noise Properties in the Japanese Islands. Entropy, 23.10.3390/e23040474 ↗
28. Lyubushin, A. (2023). Spatial Correlations of Global Seismic Noise Properties. Appl. Sci., 13.10.20944/preprints202304.1036.v1 ↗
29. Jolliffe, I.T. (1986). Principal Component Analysis, Springer.10.1007/978-1-4757-1904-8 ↗