Strong Motion Seismology

Anderson, John G.

doi:10.1002/rog.1991.29.s2.700

Cited by 29 publications

(7 citation statements)

References 484 publications

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“…The details of this will be discussed later in a separate paper. (v) Earthquake data All the earthquakes are transient (Anderson 1991); the data are, therefore, nonstationary. For lack of alternatives, in seismology as well as earthquake engineering, most data are still processed with the Fourier analysis (Geller & Ohminato 1994;Scherbaum 1994).…”

Section: (Ii) Field Wave Datamentioning

confidence: 99%

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Huang

Shen

Long

et al. 1998

Proc. R. Soc. Lond. A

19,518

12,951

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10. Discussion 987 11. Conclusions 991 References 993A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the 'empirical mode decomposition' method with which any complicated data set can be decomposed into a finite and often small number of 'intrinsic mode functions' that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the 'instrinic mode functions' yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert spectrum.In this method, the main conceptual innovations are the introduction of 'intrinsic mode functions' based on local properties of the signal, which makes the instantaneous frequency meaningful; and the introduction of the instantaneous frequencies for complicated data sets, which eliminate the need for spurious harmonics to represent nonlinear and non-stationary signals. Examples from the numerical results of the classical nonlinear equation systems and data representing natural phenomena are given to demonstrate the power of this new method. Classical nonlinear system data are especially interesting, for they serve to illustrate the roles played by the nonlinear and non-stationary effects in the energy-frequency-time distribution.

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Section: (Ii) Field Wave Datamentioning

confidence: 99%

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Huang

Shen

Long

et al. 1998

Proc. R. Soc. Lond. A

19,518

12,951

View full text Add to dashboard Cite

show abstract

“…The separation of contributions coming from the source and the structure is not an easy matter, when looking at the seismic records. However, seismological observations of the rupture process associated with crustal earthquakes, suggest a dominant effect of the rupture process on the seismic radiation in the near source range, that is, at distances comparable to the fault length and at low frequencies ( f ≤ 1 Hz) (Heaton 1990; Anderson 1991; Koyama 1997).…”

Section: Introductionmentioning

confidence: 99%

Fault slip and rupture velocity inversion by isochrone backprojection

Festa¹,

Zollo²

2006

Geophysical Journal International

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International audienceA new technique is proposed here for the retrieval of slip images from the backprojection of high-frequency displacement records. When direct S waves are seen to be dominant in the near-source data, Green functions can be approximated by the far-field terms, as described by ray theory. Assuming that the slip rapidly reaches the final value (i.e. short slip duration), the measured displacement can be ascribed to the slip contributions lying on the corresponding isochrone on the fault plane. Here we use the far-field representation theorem to backproject on the fault plane the displacement amplitudes measured along the seismogram. Through the weighted stack of amplitude maps obtained from different stations we recover high slip zones on the fault.The resolution analysis of the backprojected images is realized with spike tests (that we refer to as 'image Green functions'), which revealed to be an useful tool for detecting and locating artificial distortions of high slip patches, due to a poor data coverage. However, when the slip is uniformly spread along the isochrones, energy is scattered everywhere on the fault, leading to defocusing effects on the final images. A partial deconvolution technique is proposed by reiterating the backprojection. An important implication of this study is that slip maps can be obtained as functions of the rupture time on the fault, that is, the method can be used to retrieve variable rupture velocity kinematic models. Since the latter parameter is not known a priori, we suggest that a data set of coupled rupture velocity and slip maps is built up and the optimal model is chosen according to a waveform fitness criterion. This procedure allows the slip inversion to be separated from the rupture velocity inversion, significantly reducing the number of parameters to be estimated. Additionally, the parametrization of the rupture velocity is done on a less dense grid than the slip.By way of example, the technique is applied to estimation of the kinematic rupture model of the 2000 Tottori earthquake (M= 6.8) , based on the inversion of near-source strong-motion data

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“…

L (X) (t) = \sum_{j = - N}^{N} a_{j} X false(t + j false)

where a j = ( N + 1 − | j |)/( N + 1) 2 . The fact that window size N can be chosen adaptively is crucial for extraction of modes with different frequencies, which is more flexible than the traditional EMD and often other methods like Fourier transform and wavelets.…”

Section: Theory and Algorithmmentioning

confidence: 99%

Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel

2011

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The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms.

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Strong Motion Seismology

Cited by 29 publications

References 484 publications

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Fault slip and rupture velocity inversion by isochrone backprojection

Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel

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