scholarly journals Measuring the 1/f spatiotemporal amplitude spectrum of the DynTex database

2021 ◽  
Vol 21 (9) ◽  
pp. 2481
Author(s):  
Zoey Isherwood ◽  
Daniel Joyce ◽  
Mohana Kuppuswamy Parthasarathy ◽  
Colin Clifford ◽  
Branka Spehar
Keyword(s):  
Amplitude Spectrum

Estimation of PSF Using Logarithmic Amplitude Spectrum and Restoration of Blurred Image

2012 ◽  
Vol 132 (2) ◽  
pp. 284-290
Author(s):  
Yuka Nagashima ◽  
Shigeru Omatu ◽  
Michifumi Yoshioka
Keyword(s):  
Amplitude Spectrum ◽  
Blurred Image

THE EMPIRICAL GREEN’S FUNCTION TECHNIQUE MODIFICATION FOR ACCELEROGRAM SYNTHESIS IN LOWSEISMICITY REGIONS

2018 ◽  
Vol 12 (5-6) ◽  
pp. 72-80
Author(s):  
A. A. Krylov
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Main Shock ◽  
Amplitude Spectrum ◽  
Strong Motion ◽  
Function Technique ◽  
Focal Region ◽  
Empirical Green’S Function ◽  
Epicentral Zone ◽  
Empirical Green's Function

In the absence of strong motion records at the future construction sites, different theoretical and semi-empirical approaches are used to estimate the initial seismic vibrations of the soil. If there are records of weak earthquakes on the site and the parameters of the fault that generates the calculated earthquake are known, then the empirical Green’s function can be used. Initially, the empirical Green’s function method in the formulation of Irikura was applied for main shock record modelling using its aftershocks under the following conditions: the magnitude of the weak event is only 1–2 units smaller than the magnitude of the main shock; the focus of the weak event is localized in the focal region of a strong event, hearth, and it should be the same for both events. However, short-termed local instrumental seismological investigation, especially on seafloor, results usually with weak microearthquakes recordings. The magnitude of the observed micro-earthquakes is much lower than of the modeling event (more than 2). To test whether the method of the empirical Green’s function can be applied under these conditions, the accelerograms of the main shock of the earthquake in L'Aquila (6.04.09) with a magnitude Mw = 6.3 were modelled. The microearthquake with ML = 3,3 (21.05.2011) and unknown origin mechanism located in mainshock’s epicentral zone was used as the empirical Green’s function. It was concluded that the empirical Green’s function is to be preprocessed. The complex Fourier spectrum smoothing by moving average was suggested. After the smoothing the inverses Fourier transform results with new Green’s function. Thus, not only the amplitude spectrum is smoothed out, but also the phase spectrum. After such preliminary processing, the spectra of the calculated accelerograms and recorded correspond to each other much better. The modelling demonstrate good results within frequency range 0,1–10 Hz, considered usually for engineering seismological studies.

Regional attenuation models in Central and Eastern North America using the NGA-East database

2021 ◽  
pp. 875529302110187
Author(s):  
Jeff Bayless
Keyword(s):  
North America ◽  
Frequency Dependence ◽  
Gulf Coast ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Amplitude Spectrum ◽  
Active Regions ◽  
Atlantic Coastal Plain ◽  
Eastern North America ◽  
Anelastic Attenuation

The anelastic attenuation term found in ground motion prediction equations (GMPEs) represents the distance dependence of the effect of intrinsic and scattering attenuation on the wavefield as it propagates through the crust and contains the frequency-dependent quality factor, [Formula: see text], which is an inverse measure of the effective anelastic attenuation. In this work, regional estimates of [Formula: see text] in Central and Eastern North America (CENA) are developed using the NGA-East regionalization. The technique employed uses smoothed Fourier amplitude spectrum (FAS) data from well-recorded events in CENA as collected and processed by NGA-East. Regional [Formula: see text] is estimated using an assumption of average geometrical spreading applicable to the distance ranges considered. Corrections for the radiation pattern effect and for site response based on [Formula: see text] result in a small but statistically significant improvement to the residual analysis. Apparent [Formula: see text] estimates from multiple events are combined within each region to develop the regional models. Models are provided for three NGA-East regions: the Gulf Coast, Central North America, and the Appalachian Province. Consideration of the model uncertainties suggests that the latter two regions could be combined. There were not sufficient data to adequately constrain the model in the Atlantic Coastal Plain region. Tectonically stable regions are usually described by higher [Formula: see text] and weaker frequency dependence ([Formula: see text]), while active regions are typically characterized by lower [Formula: see text] and stronger frequency dependence, and the results are consistent with these expectations. Significantly different regional [Formula: see text] is found for events with data recorded in multiple regions, which supports the NGA-East regionalization. An inspection of two well-recorded events with data both in the Mississippi embayment and in southern Texas indicates that the Gulf Coast regionalization by Cramer in 2017 may be an improvement to that of NGA-East for anelastic attenuation. The [Formula: see text] models developed serve as epistemic uncertainty alternatives in CENA based on a literature review and a comparison with previously published models.

scholarly journals The magnitude of center-surround facilitation in the discrimination of amplitude spectrum is dependent on the amplitude of the surround

2011 ◽  
Vol 11 (7) ◽  
pp. 14-14 ◽  
Author(s):  
A. P. Johnson ◽  
B. Richard ◽  
B. C. Hansen ◽  
D. Ellemberg
Keyword(s):  
Amplitude Spectrum

Selection of random vibration theory procedures for the NGA-East project and ground-motion modeling

2021 ◽  
Vol 37 (1_suppl) ◽  
pp. 1420-1439
Author(s):  
Albert R Kottke ◽  
Norman A Abrahamson ◽  
David M Boore ◽  
Yousef Bozorgnia ◽  
Christine A Goulet ◽  
...  
Keyword(s):  
Ground Motion ◽  
Time Domain ◽  
Random Vibration ◽  
Amplitude Spectrum ◽  
Motion Modeling ◽  
Peak Response ◽  
Vibration Theory ◽  
Random Vibration Theory ◽  
The Time Domain ◽  
The Impact

Traditional ground-motion models (GMMs) are used to compute pseudo-spectral acceleration (PSA) from future earthquakes and are generally developed by regression of PSA using a physics-based functional form. PSA is a relatively simple metric that correlates well with the response of several engineering systems and is a metric commonly used in engineering evaluations; however, characteristics of the PSA calculation make application of scaling factors dependent on the frequency content of the input motion, complicating the development and adaptability of GMMs. By comparison, Fourier amplitude spectrum (FAS) represents ground-motion amplitudes that are completely independent from the amplitudes at other frequencies, making them an attractive alternative for GMM development. Random vibration theory (RVT) predicts the peak response of motion in the time domain based on the FAS and a duration, and thus can be used to relate FAS to PSA. Using RVT to compute the expected peak response in the time domain for given FAS therefore presents a significant advantage that is gaining traction in the GMM field. This article provides recommended RVT procedures relevant to GMM development, which were developed for the Next Generation Attenuation (NGA)-East project. In addition, an orientation-independent FAS metric—called the effective amplitude spectrum (EAS)—is developed for use in conjunction with RVT to preserve the mean power of the corresponding two horizontal components considered in traditional PSA-based modeling (i.e., RotD50). The EAS uses a standardized smoothing approach to provide a practical representation of the FAS for ground-motion modeling, while minimizing the impact on the four RVT properties ( zeroth moment, [Formula: see text]; bandwidth parameter, [Formula: see text]; frequency of zero crossings, [Formula: see text]; and frequency of extrema, [Formula: see text]). Although the recommendations were originally developed for NGA-East, they and the methodology they are based on can be adapted to become portable to other GMM and engineering problems requiring the computation of PSA from FAS.

Abstract 16924: Initial AMSA and Increased AMSA After CPR Predict Second Shock Success in Initially Shock-resistant VF During Out-of-hospital Cardiac Arrest

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Ulrich Herken ◽  
Weilun Quan
Keyword(s):  
Logistic Regression ◽  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Success Rate ◽  
Fourier Transformation ◽  
Amplitude Spectrum ◽  
Fast Fourier Transformation ◽  
Shock Delivery ◽  
Spectrum Area ◽  
Hospital Cardiac Arrest

Purpose: Amplitude spectrum area (AMSA), which is calculated from the ventricular fibrillation (VF) waveform using fast Fourier transformation, has been recognized as a predictor of successful defibrillation (DF) and as an index of myocardial perfusion and viability during resuscitation. In this study, we investigated whether a change in AMSA occurring during CPR would predict DF outcome for subsequent DF attempts after a failed DF. We hypothesized that a patient responding to CPR with an increase in AMSA would have an increased likelihood of DF success. Methods: This was a retrospective analysis of out-of-hospital cardiac arrest patients who received a second DF due to initially shock-resistant VF. A total of 193 patients with an unsuccessful first DF were identified in a manufacturer database of electrocardiographic defibrillator records. AMSA was calculated for the first DF (AMSA1) and the second DF (AMSA2) during a 2.1 sec window ending 0.5 sec prior to DF. A successful DF attempt was defined as the presence of an organized rhythm with a rate ≥ 40 / min starting within 60 sec from the DF and lasting for > 30 sec. After the failed first DF, all patients received CPR for 2 to 3 minutes before delivery of the second DF. Change in AMSA (dAMSA) was calculated as dAMSA = AMSA2 - AMSA1. Results: The overall second DF success rate was 14.5%. Multivariable logistic regression showed that both AMSA1 and dAMSA were independent predictors of second DF success with odds ratios of 1.24 (95% CI 1.12 - 1.38, p<0.001) and 1.27 (95% CI 1.16 - 1.41, p<0.001) for each mVHz change in AMSA or dAMSA, respectively. Conclusions: In initially DF-resistant VF, a high initial AMSA value predicted an increased likelihood of second shock success. An increase of AMSA in response to CPR also predicted a higher second shock success rate. Monitoring of AMSA during resuscitation therefore may be useful to guide CPR efforts, possibly including timing of second shock delivery. These findings also further support the value of AMSA as indicator of myocardial viability.

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