Testing Kalman smoothing/PCA transient signal detection using synthetic data

Seismological Research Letters

Volumn 84, Issue 3, 2013, Pages 433-443

  Ji, Kang Hyeun ^a   Herring, Thomas A ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTION ALGORITHM; DETECTION CAPABILITY; MULTIPLE COMPONENTS; NETWORK TRANSFORMATION; NOISE CONTRIBUTIONS; SAMPLE EIGENVECTORS; SIGNALS IN SPACES; SIMULATED DATASETS;

ALGORITHMS; MICROCOMPUTERS; SIGNAL TO NOISE RATIO; STATISTICS; WHITE NOISE;

SIGNAL DETECTION;

ALGORITHM; DATA SET; EARTHQUAKE PREDICTION; KALMAN FILTER; PRINCIPAL COMPONENT ANALYSIS; SEISMIC NOISE; SMOOTHING;

EID: 84878155390     PISSN: 08950695     EISSN: 19382057     Source Type: Journal    
DOI: 10.1785/0220120155     Document Type: Article

References (14)

1. Agnew, D. C. (2013). Realistic simulations of geodetic network data: The Fakenet package, Seismol. Res. Lett. 84, no. 3, 426-432.
2. Allen, M. R., and L. A. Smith (1996). Monte Carlo SSA: Detecting irregular oscillations in the presence of colored noise, J. Clim. 9, 3373-3404.
3. Dong, D., P. Fang, Y. Bock, F. Webb, L. Prawirodirdjo, S. Kedar, and P. Jamason (2006). Spatiotemporal filtering using principal component analysis and Karhunen-Loeve expansion approaches for regional GPS network analysis, J. Geophys. Res. 111, B03405, doi: 10.1029/2005JB003806.
4. Ji, K. H., and T. A. Herring (2011). Transient signal detection using GPS measurements: Transient inflation at Akutan volcano, Alaska, during early 2008, Geophys. Res. Lett. 38, L06307, doi: 10.1029/2011GL046904.
5. Ji, K. H., and T. A. Herring (2013). A method for detecting transient signals in GPS position time series: Smoothing and principal component analysis, Geophys. J. Int., doi: 10.1093/gji/ggt003.
6. Langbein, J. (2008). Noise in GPS displacement measurements from Southern California and Southern Nevada, J. Geophys. Res. 113, B05405, doi: 10.1029/2007JB005247.
7. Lohman, R., and J. Murray (2013). The SCEC geodetic transient detection validation exercise, Seismol. Res. Lett. 84, no. 3, 419-425.
8. McGuire, J. J., and P. Segall (2003). Imaging of aseismic fault slip transients recorded by dense geodetic networks, Geophys. J. Int. 155, 778-788, doi: 10.1111/j.1365-246X.2003.02022.x.
9. Murray-Moraleda, J., and R. Lohman (2010). Workshop targets development of geodetic transient detection methods, Eos Trans. AGU 91, 58, doi: 10.1029/2010EO060008.
10. Ohtani, R., J. J. McGuire, and P. Segall (2010). Network strain filter: A new tool for monitoring and detecting transient deformation signals in GPS arrays, J. Geophys. Res. 115, B12418, doi: 10.1029/2010JB007442.
11. Segall, P., and M. Matthews (1997). Time-dependent inversion of geodetic data, J. Geophys. Res, 102, 22,391-22,409, doi: 10.1029/97JB01795.
12. Wdowinski, S., Y. Bock, J. Zhang, P. Fang, and J. Genrich (1997). Southern California permanent GPS geodetic array: Spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake, J. Geophys. Res. 102, 18,057-18,070, doi: 10.1029/97JB01378.
13. Wessel, P., and W. H. F. Smith (1998). New, improved version of the Generic Mapping Tools released, Eos Trans. AGU 79, 579.
14. Williams, S. D. P., Y. Bock, P. Fang, P. Jamason, R. M. Nikolaidis, L. Prawirodirdjo, M. Miller, and D. J. Johnson (2004). Error analysis of continuous GPS position time series, J. Geophys. Res. 109, B03412, doi: 10.1029/2003JB002741.