Generation of artificial earthquake records with a nonstationary Kanai–Tajimi model

Rofooei, Fayaz R.; Mobarake, Aghababaii; Ahmadi, Goodarz

doi:10.1016/s0141-0296(00)00093-6

Cited by 88 publications

(31 citation statements)

References 14 publications

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“…In this research, ξ g is assumed to be a constant and 0.3 is used based on previous works (Fan and Ahmadi,; Rofooei et al., ) and the time‐evolution of

ω_{normalg} (t)

is determined via a wavelet technique that moves from the beginning to the end of the ground motion. The time varying frequencies are determined via the pseudo frequency of the basis wavelets.…”

Section: Proposed Methodsmentioning

confidence: 99%

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

Amiri

Rad

Hazaveh

2014

Computer aided Civil Eng

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“…In this research, ξ g is assumed to be a constant and 0.3 is used based on previous works (Fan and Ahmadi,; Rofooei et al., ) and the time‐evolution of

ω_{normalg} (t)

is determined via a wavelet technique that moves from the beginning to the end of the ground motion. The time varying frequencies are determined via the pseudo frequency of the basis wavelets.…”

Section: Proposed Methodsmentioning

confidence: 99%

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

Amiri

Rad

Hazaveh

2014

Computer aided Civil Eng

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“…The proposed method includes a number of major steps. In the first step, the peak ground accelerations (PGAs) of all accelerograms were scaled to 1 g. Afterward, the duration of the earthquake record is obtained by eliminating the beginning and final part of the accelerogram containing less than 5% of the cumulative energy of the original record (Rofooei et al, 2001). Then, wavelet packet coefficients of the accelerograms were calculated at suitable DWPT decomposition levels ( J ).…”

Section: The Proposed Methodsmentioning

confidence: 99%

Generation of Near‐Field Artificial Ground Motions Compatible with Median‐Predicted Spectra Using PSO‐Based Neural Network and Wavelet Analysis

Amiri

Rad

Aghajari

et al. 2012

Computer aided Civil Eng

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The principal purpose of this article is to present a novel method based on particle swarm optimization (PSO) and wavelet packet transform (WPT) techniques and multilayer feed‐forward (MLFF) neural networks, in order to generate spectrum‐compatible near‐field artificial earthquake accelerograms. PSO is employed to optimize the weight values of the networks. Moreover, to improve the training efficiency principal component analysis (PCA) is used, as a reduction technique. The proposed PSO‐based MLFF (PSOBMLFF) neural network develops an inverse mapping from compacted and reduced spectrum coefficients into metamorphosed accelerogram wavelet packet coefficients. In this research, to produce compatible synthetic long‐period near‐field ground motions with median predicted spectra, the attenuation of peak ground velocity (PGV) with the close horizontal distance (R), moment magnitude (M), and time‐average shear‐wave velocity from the surface to 30 m (Vs30) has been developed using nonlinear regression analysis.

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“…For this experiment a MATLAB simulator based on the seismic models of [29] was used. For an SNR = À3 dB, Figure 5 shows the seismic signal and its filtered version using 2MM EKF with Rossler x-component as a model.…”

Section: Experiments Four: Seismic Signalsmentioning

confidence: 99%

Ubiquitous Filtering for Nonlinear Problems

Kontorovich¹,

Ramos-Alarcon²

2020

Research Advances in Chaos Theory

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This chapter develops and extends the general theoretical results, previously published in the chapter "Nonlinear filtering of weak chaotic signals", and presents detailed implementations of a computationally simple, robust (filtering fidelity almost insensitive to changes of the desired input signal properties) and rather precise approach for the filtering of weak signals of different physical nature (biological, seismic, voice, etc.) in presence of white Gaussian noise. The implementations rely on non-linear filtering techniques that in general can be considered as either one-moment or multi-moment, in the sense that they operate with a single sample (instantaneous fashion) or with various adjacent samples (non-instantaneous fashion). Chaotic modeling of the real input signals allows achieving an almost ubiquitous filtering approach with a computationally simple implementation. Application of the linearization strategies (for both one and twomoment filtering) provide, additionally, "invariance" of the processing algorithms to variations on the nature and statistics of the input signals.

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Generation of artificial earthquake records with a nonstationary Kanai–Tajimi model

Cited by 88 publications

References 14 publications

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

Generation of Near‐Field Artificial Ground Motions Compatible with Median‐Predicted Spectra Using PSO‐Based Neural Network and Wavelet Analysis

Ubiquitous Filtering for Nonlinear Problems

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